IMS Project Portfolio - 2015

NSF I/UCRC For Intelligent Maintenance Systems
University of Cincinnati P 1.513.556.3412
PO Box 210072 F 1.513.556.3390
Cincinnati, OH 45221 www.imscenter.net
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IMS Project Portfolio
Center for Intelligent Maintenance Systems (IMS)
University of Cincinnati
February 2015
Copyright © 2015

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Contents
1.1 The Evolution of Maintenance Practices......................................................................................4
1.2 Unmet Needs in Current Maintenance Service Practices ......................................................5
1.2.1 Equipment Intelligence ..............................................................................................................5
1.2.2 Synchronization Intelligence....................................................................................................7
1.2.3 Operations Intelligence ..............................................................................................................7
1.3 The Future of Maintenance Service................................................................................................7
1.4 The IMS Center: Methodology and Tools.....................................................................................8
1.4.1 IMS Methodology: ‘5S’ Systematic Approach.....................................................................9
1.4.2 IMS Tools: Watchdog Agent®............................................................................................... 10
2.1 Industrial Projects.............................................................................................................................. 13
2.1.1 Project Collaboration with TOYOTA................................................................................... 13
2.1.2 Project Collaboration with GENERAL MOTORS (G.M.) ............................................... 16
2.1.3 Project Collaboration with HARLEY-DAVIDSON MOTOR COMPANY (HDMC).. 17
2.1.4 Project Collaboration with KOMATSU............................................................................... 18
2.1.5 Project Collaboration with CATERPILLAR (Phase 1) .................................................. 19
2.1.6 Project Collaboration with CATERPILLAR (Phase 2) .................................................. 21
2.1.7 Project Collaboration with ALSTOM TRANSPORT........................................................ 21
2.1.8 Project Collaboration with PARKER-HANNIFIN............................................................ 26
2.1.9 Project Collaboration with OMRON.................................................................................... 27
2.1.10 Project Collaboration with GENERAL ELECTRIC (GE) ............................................. 29
2.1.11 Hong Kong’s Airport Chiller Health Monitoring.......................................................... 31
2.1.12 Project Collaboration with TECHSOLVE ........................................................................ 33
2.1.13 Intelligent Prognostic Tools for Smart Machine Platform....................................... 35
2.1.14 Project Collaboration with SIEMENS............................................................................... 40
2.1.15 Project Collaboration with Hiwin, Taiwan.................................................................... 41
2.1.16 Project with INSTITUTE FOR INFORMATION INDUSTRY (III), TAIWAN ......... 44
2.1.17 Project Collaboration with FMTC...................................................................................... 49
2.1.18 Project Collaboration with Korea Aerospace University (KAU) ........................... 50
2.1.19 Project Collaboration with Cosen ..................................................................................... 50
2.1.20 Project Collaboration with Daniel Drake Center for Post-Acute Care and
Beechwood Home..................................................................................................................................... 53

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2.2 Fundamental Research Projects................................................................................................... 58
2.2.1 A Systematic Methodology for Data Validation and Verification for Prognostics
Applications................................................................................................................................................ 58
2.2.2 Hybrid Modeling of Equipment Efficiency and Health for Advanced Servo-drive
Presses.......................................................................................................................................................... 60
2.2.3 Battery Prognostics................................................................................................................... 63
2.2.4 Remaining Useful Life Prediction of In-vehicle Battery Pack................................... 66
2.2.5 Virtual Bearing............................................................................................................................ 68
2.2.6 Couple model............................................................................................................................... 69
2.3 PHM Society Data Challenges........................................................................................................ 72
2.3.1 [PHM 2008] A Similarity-based Prognostics Approach for Remaining Useful Life
Estimation of Engineered Systems.................................................................................................... 72
2.3.2 [PHM 2009] Gearbox Health Assessment and Fault Classification ........................ 74
2.3.3 [PHM 2010] Remaining Useful Life Estimation of High-speed CNC Milling
Machine Cutters ........................................................................................................................................ 76
2.3.4 [PHM 2011] Fault Detection of Anemometers............................................................... 79
2.3.5 (2014) An Effective Predictive Maintenance Approach based on Historical
Maintenance Data using a Probabilistic Risk Assessment........................................................ 81
2.4 Biography: Professor Jay Lee......................................................................................................... 85

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1. Introduction
1.1 The Evolution of Maintenance Practices
The traditional approach for maintaining the industrial machines and processes has been one
that is purely reactive. Mitigating procedures are performed to the equipment once it breaks
down. This, as most companies have found out, is a maintenance paradigm with many
disadvantages. When a machine is run to failure, i.e. until one of its parts fail making it
inoperable, produced parts may not meet quality standards, or the degradation of the other
components might have just been accelerated unknowingly, or worst, the cost of restoring the
machine to an acceptable and sustained operational state might be more expensive than a
purchasing a replacement unit. There is also a requirement to store numerous spare parts.
Finally, since a machine can break down at any time, maintenance and production operations
are not scheduled efficiently, thus, a huge maintenance crew is needed to achieve high
production throughput rates.
An improved strategy that companies employ is preventive maintenance. Many of the machine
manufacturers and suppliers provide component lifetime and maintenance guidelines to their
customers. With this method, maintenance activities can be performed so as not to interfere
with production schedule. There is no need to store a large inventory of spare parts because
they can be ordered at regular intervals just before the parts need to be replaced. The primary
advantage of using preventive maintenance is a high overall equipment efficiency (OEE) score,
but, it does so at a very high cost. To be able to capture the majority of impending component
failures, maintenance personnel must replace or condition the parts at closer time intervals,
which mean that spare parts must be purchased more frequently.
Nowadays, many manufacturing companies are adopting condition-based maintenance or CBM.
Predictive Maintenance (PdM) is a right-on-time maintenance strategy. It is based on the failure
limit policy in which maintenance is performed only when the failure rate, or other reliability
indices, of a unit reaches a predetermined level. This maintenance strategy has been
implemented as Condition Based Maintenance (CBM) in most production systems, where
certain performance indices are periodically (Barbera et al. 1996; Chen et al. 2002) or
continuously monitored (Marseguerra et al. 2002). Whenever an index value crosses its
predefined threshold, maintenance actions are performed to restore the machine to its original
state, or to a state where the changed value is at a satisfactory level in comparison to the
threshold.
Predictive maintenance (PdM) can be best described as a process that requires both
technology and human skills, while using a combination of all available diagnostic and
performance data, maintenance history, operator logs and design data to make timely decisions
about maintenance requirements of major/critical equipment. It is this integration of various
data, information and processes that leads to the success of a PdM program. It analyzes the
trend of measured physical parameters against known engineering limits for the purpose of
detecting, analyzing and correcting a problem before a failure occurs. A maintenance plan is
made based on the prediction results derived from condition-based monitoring. This method can
cost more up front than PM because of the additional monitoring hardware and software
investment, cost of manning, tooling, and education that is required to establish a PdM program.
However, it provides a basis for failure diagnostics and maintenance operations, and offers

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increased equipment reliability and a sufficient advance in information to improve planning,
thereby reducing unexpected downtime and operating costs. This approach takes into account
the current state or condition of the component, thereby reducing the number of unscheduled
breakdowns by monitoring the equipment condition to predict failures so that remedial fixes can
be performed. Using built-in or add-on sensors, signal measurements are extracted to describe
the component’s current performance. Then, this information is compared with previously
collected performance history to determine whether a deviation has occurred to suggest that the
equipment is at fault or showing symptoms that can lead to an impending failure. The apparent
shortcoming of CBM is that it simply detects that the equipment is “at fault” or “nearing a fault”
condition. It does not elaborate on the type of failure the equipment has experienced and it does
not identify what component is faulty. The information from a CBM system does not provide
much insight to the maintenance personnel on what to fix in that machine. Predictive
maintenance has been developed as an improvement to CBM so as to infer within a horizon
time when the equipment will fail. Nonetheless, it inherited the same problems as the CBM
method in pinpointing the type of fault and the critical component at fault.
1.2 Unmet Needs in Current Maintenance Service Practices
After reviewing the evolution of the different maintenance paradigms, there exist key issues that
need to be addressed in order to achieve the maximum availability and efficiency from important
company assets. These unmet needs are summarized in Figure 1 and are explained in detail in
the succeeding sections.
1.2.1 Equipment Intelligence
There is indeed a need to migrate towards another maintenance paradigm – intelligent
prognostics. It transcends being merely a predictive maintenance system by exactly identifying
which component of a machine is likely to fail, and when and highlight the event to trigger
maintenance service and order spare parts. [2] A key principle in intelligent maintenance is the
assessment and prediction of the performance degradation of a process, machine or service. [3]
By extracting critical features from signal measurements, performance degradation can be used
to predict unacceptable equipment performance before it occurs. With the use of enabling and
transformation technologies, plant maintenance can be shifted from the “fail-and-fix” tradition
into the “predict-and-prevent” principle as shown in Figure 2.

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Figure 1. Unmet Needs in Maintenance Services
Figure 2. Maintenance Service Paradigm Shift from Fail-and-Fix to Predict-and-Prevent
By utilizing sensors and embedded systems, the intelligent prognostics approach shall allow the
critical asset to be able to automatically trigger itself to acquire appropriate health indicators
from its critical machine parts when needed, assess the features from the temporal health data,
diagnose itself to determine its current state of machine performance, infer whether significant
performance degradation has been detected, identify the imminent fault it is going to experience
and predict when failure is going to occur and automatically call for maintenance crew for
conditioning. These capabilities just mentioned are essential to the future of maintenance
service where machines are capable of self-assessment or self-diagnostics and ultimately, self-
maintenance and even self-healing. Self-healing can be achieved when the machine is supplied
with appropriate control logic to follow whenever its components have reached specific
degradation levels such as shutting down secondary subsystems that are not critical to its basic
functionalities to reduce load and system power consumption.

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1.2.2 Synchronization Intelligence
Currently most of the machine data, if available, is either hidden or kept in the machine. Thus,
assets in a manufacturing plant become islands of health indicators and are not being accessed
and analyzed for health information. If ever the data undergoes a certain degree of processing,
the information that is extracted unfortunately remains trapped in the machine. There is a
recognized barrier between the production floor and the management level that inhibits critical
machine and process health information to be transmitted so that the latter can make more
informed and timely decisions on the machines. What is needed is an infrastructure that enables
the health information to be seamlessly transmitted to a higher manufacturing level for decision
making, or to other similar machines for peer-to-peer equipment performance comparison.
When machine performance information is sent up the production business unit, comprehensive
and real-time decisions can be made on the manufacturing assets such as production
scheduling (assigning appropriate order sizes to high performing machines), maintenance
scheduling (with a sufficient horizon time, conditioning or mitigating procedures can be
performed while not interfering with production runs) and even machine-part matching
(assigning the “best” machine that will meet quality requirements of a particular produced part).
1.2.3 Operations Intelligence
When the company assets are now capable of self-diagnostics and self-maintenance, and the
health information (both machine and process) can be shared very easily most especially to the
different business functions, then there lies an opportunity to achieve intelligent operations in
the manufacturing facility.
With the machine health information, one is enabled to predict and prioritize maintenance
activities, and when coupled with the process health information, production can be optimized
either to achieve desired volume orders and/or part quality requirements. Thus, the company
achieves intelligent operations in the process because they are now capable of producing
quality parts while running at a near-zero unexpected downtime run.
The missing component to achieving such a goal is an optimized decision making logic modeled
on plant operations that integrates production and maintenance activities without interfering with
each other.
1.3 The Future of Maintenance Service
In summary, the key issues of the current manufacturing maintenance practices have been
defined: (1) equipment intelligence through self-maintenance – to be able to extract machine
and process health and eventually predict machine performance degradation; (2)
synchronization intelligence – refers to the seamless communication between the production
floor and management unit so that timely and informed decisions can be made based on
machine and process health information; and (3) operations intelligence – the ability to leverage
on self-maintenance and intelligent synchronization to efficiently schedule production and
maintenance to achieve a near-zero downtime manufacturing operations.
The Center for Intelligent Maintenance Systems (IMS) has been in the pursuit to address these
key issues by performing relevant research in order to develop the enabling technologies for the
different aspects of self-maintenance, intelligent synchronization and intelligent operations.

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Figure 3 summarizes the vision of the IMS Center while providing a directed plan on how to
solve these maintenance opportunities.
Figure 3. IMS Center Vision of Intelligent Maintenance
From the IMS vision, degradation information from the critical assets can be used to predict their
performance to enable near-zero downtime performance. The asset degradation data can also
be fed back to the design stage of the equipment for assessing its reusability and predicting its
useful life after disassembly and reuse.
To address the key issues mentioned earlier, the IMS Center has recognized that the following
technologies need to be developed:
 Processing system that extracts machine data, and convert this voluminous data space
into a simple but comprehensive health information;
 Communication infrastructure that can automatically relay health information to
appropriate business functions within the company; and
 Decision-making system that uses the health information to make informed business
decisions on the company assets.
1.4 The IMS Center: Methodology and Tools
The IMS Center is an Industry/University Research Cooperative Center (I/UCRC) for Intelligent
Maintenance Systems that has been founded to serve as a center of excellence for the creation
and dissemination of a systematic body of knowledge in intelligent e-maintenance systems and
ultimately to impact next-generation product, manufacturing, and service systems. The IMS
Center serves as a catalyst as well as enabler to assist company members to transform their

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operation strategies from today’s “Fail-to-Fix/Fly-to-Fix (FAF)” to “Predict-and-Prevent (PAP)”
performance. The IMS Center brings value to its members by validating high-impact emerging
technologies as well as by harnessing business alliances through collaborative test-beds.
Through this end, the IMS Center shall enable products and systems to achieve and sustain
near-zero breakdown performance, and ultimately transform maintenance data to useful
information for improved closed-loop product life cycle design and asset management. The IMS
Center is focused on frontier technologies in embedded and remote monitoring, prognostics
technologies, and intelligent decision support tools and has coined the trademarked Watchdog
Agent prognostics tools and Device-to-Business (D2B) Infotronics platform for e-maintenance
systems.
1.4.1 IMS Methodology: ‘5S’ Systematic Approach
Addressing future maintenance services necessitates a systematic “5S” approach (see Figure
4). This approach was devised by IMS Center in order to develop and research all aspects of
future maintenance infrastructures. This systematic approach consists of five key elements:
 Streamline: this encompasses techniques for sorting, prioritizing, and classifying data into
more feature-based health clusters. This may also include reducing large data sets (from
both maintenance history and on-line data DAQ) to smaller dimensions, leading to
correlation of the relevant data to feature maps for better data representation.
 Smart Process: using the right Watchdog Agent®
tool for the right application. This requires
techniques for selecting appropriate prognostics tools based on application conditions,
criticality of each conditions for machine health, and system requirements.
 Synchronize: converting component data to component degradation information at the local
level and further predicting trends of health using a visualized radar chart for decision-ready
information. Maintenance data is transformed to health information and to an automated
action (i.e. order parts, schedule maintenance based on criticality of component or
machine). This process ensures a key characteristic of “Only Handle Information Once”
(OHIO).
 Standardize: creation of a standardized information structure for equipment condition data
and health information so that it is compatible with higher-level business systems and
enables the information to be embedded in business ERP and asset management systems.
The goal here is to keep the process as a standard approach for day-to-day practices.
 Sustain: utilizing the transformed data for information-level decision making. System
information is then shared amongst all stages of product and business life cycle systems:
product design, manufacturing, maintenance, service logistics, and others in order to realize
a closed-loop product life-cycle design and continuous improvement of product-level quality
and business-level performance.

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Figure 4. IMS 5S Systematic Methodology
1.4.2 IMS Tools: Watchdog Agent®
Since 2000, the IMS Center has been spearheading the development of a processing system
called the Watchdog Agent® in a bid to address the first issue. Simply put, the Watchdog
Agent® is an enabling technology that shall allow for a successful implementation of intelligent
maintenance. The toolbox is a collection of algorithms that can be used to assess and predict
the performance of a process or equipment based on input from sensors, historical data and
operating conditions. Referring to Figure 5, the algorithms can be classified into four major
categories: signal processing and feature extraction, quantitative health assessment,
performance prediction and condition health diagnosis. Performance-related information can be
further extracted via multiple sensor inputs through signal processing, feature extraction and
sensor fusion techniques. The historical behavior of process signatures can be utilized to predict
future behavior and thus enable forecasting of the process’s or machine’s performance.
Proactive maintenance can therefore be facilitated through the prediction of potential failures
before they occur.
By leveraging on current technologies, the Watchdog Agent® can either exist in Matlab or
LabVIEW platform although development is underway to seat the Watchdog Agent® in PLC
controller and C-based platforms. This has been done to address the diversity of platform that
are currently being used in most manufacturing facilities.

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Figure 5. Software Architecture and Visualization Tools of Watchdog Agent® Toolbox

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2. IMS Research and Project Portfolio
This section briefly summarizes several projects and test-beds where the Watchdog Agent®
has
been used. The general methodology of IMS intelligent prognostics is still being applied; only
the algorithms are selected and reconfigured depending on the monitoring task being
implemented.
The Center for Intelligent Maintenance Systems (IMS) is working with its research partners to
file joint patents and technology (IP) transfer for industrial applications. Examples of such efforts
are IMS activities with Parker-Hannifin, Techsolve, and Siemens.
INDUSTRY COMPANY PROJECT
Automotive
Toyota Surge map modeling for air compressors
General Motors Prognostics of vehicle components
Harley Davidson Spindle bearing monitoring
Heavy Machinery
Komatsu
Improving the diagnostic and prognostic monitoring
capabilities for Komatsu’s truck diesel engines
Caterpillar Machine Tool health monitoring
ALSTOM Transport Feasibility study of PHM applications
Process
Proctor and Gamble Quality-centric process health management
Parker Hydraulic hose prognostics
Omron Precision energy management systems
General Electric Intelligent Prognostic System for Machine Health
Monitoring
Hong Kong Electrical and
Mechanical Services
Department (EMSD)
Hong Kong Airport’s Chiller Health Monitoring
Consulting
Techsolve Smart machine platform initiative (SMPI)
Techsolve Intelligent Prognostic Tools for Smart Machine
Platform
Siemens Reconfigurable plug-n-prognose Watchdog Agent®
There have also been some researches apart from the industry including the PHM data
challenges and NSF fundamental researches. They are summarized in the table below:

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Research Title
Fundamental
Researches
A Systematic Methodology for
Data Validation and Verification
for Prognostics Applications
In collaboration with IMS
center at University of
Michigan and Missouri
University of Science and
Technology
Hybrid modeling of equipment
efficiency and health for
advanced servo-drive presses
In collaboration with Center
for Precision Forming at
Ohio State University
Industrial Energy Monitoring In collaboration with Omron
Battery Prognostics
RUL prediction of the in-vehicle
battery pack
In collaboration with
Shanghai Jiao Tong
University
Virtual Bearing
PHM Society Data
Challenges
[PHM 2008] A similarity-based
prognostics approach for
Remaining Useful Life estimation
of engineered systems
1
st
and 3
rd
Rank
[PHM 2009] Gearbox Health
Assessment and Fault
Classification
1
st
and 2
nd
Ranks
[PHM 2010] RUL estimation of
high-speed CNC milling machine
cutters
3
rd
Ranks
[PHM 2011] Anemometer fault
detection
1
st
and 3
rd
Ranks
2.1 Industrial Projects
2.1.1 Project Collaboration with TOYOTA
a) General Background:
This project is conducted between the Center for IMS and Toyota Motors Manufacturing,
Kentucky, Inc. (TMMK), the objective of which is to improve the accuracy and efficiency of surge
avoidance control for centrifugal air compressors. The main achievements of the project work is

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to use data-driven tools to model an improved surge map, and provide positive feedback control
of inlet guide vane (IGV) to reduce the rate of surge events in the manufacturing facility of
TMMK.
b) Project Duration:
February 2005 – December 2007
c) Project Objectives:
The objective of this research was to develop a data-driven modeling based approach for
obtaining surge maps for air compressors under variable operating conditions so as to achieve
surge prevention with high efficiency.
d) Summary of Technical Approach:
Surge refers to a phenomenon of large oscillating reverse flow when compressor is operated
under low flow rate and high pressure. Surge leads to costly damage of the compressor and
downtime of the system powered by the compressor. To prevent the damage, a surge map is
widely used where a surge line is defined to separate surge and not-surge operation (Figure 6).
Figure 6. Surge Maps for Compressors
However, there is a challenge that exact location of the surge line cannot be accurately located
due to the fact that large number of variables related to compressor operation, including
ambient air conditions can affect the occurrence of surge. Hence the operators would have to
operate the compressor far from the surge line to avoid the possibility of experiencing surge,
which on the other hand would decrease compressor efficiency. In order to operate the
compressor at optimal efficiency while preventing surge, a data-driven method is used to
develop an improved surge map model for actual operation in the specific environment.

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Figure 7. Data-driven Surge Map Modeling
Figure 7 shows the flowchart of the data-driven approach.
1) Multivariate measurements are synchronously sampled under known surge or not-
surge conditions. Variables include pressure readings, flow rate of airflow, electric
current, IGV feedback, IGV command, humidity, temperature readings etc.
2) Principal Component Analysis (PCA) is employed to reduce the dimension of the
dataset acquired, by projecting the dataset to a new coordination system while
retaining as much variance of the original dataset as possible.
3) Support Vector Machine (SVM) is used to train a classification model based on the
transformed data, or principal components. SVM finds the separating planes that
maximize the distance between the two classes of surge & not-surge, and therefore
can potentially minimize the misclassification.
During around 80 days of data acquisition, 25 surge and 22 not-surge data points were
collected and 15 from each class are used for training the separation plane. For the training
dataset, there is no misclassification hence there is no unpredicted surge and false alarms. The
rest data points were used to test model performance and there were 6 false alarms among the
22 not-surge data. The model was further developed and used for open-loop surge avoidance
control.
e) Project Deliverables and Results:
Eventually as deliverables of the project, a validated IMS tool is ported into existing compressor
controller. The compressor manufacturer that supplies for TMMK was suggested to provide
such capability of on-line surge detection and control optimization. Existing machines were
retrofitted with surge detection technique, which saves the facility around 50,000 US dollars per
year as opposed to actual project budget 100,000. The project is recognized as a success story
and promoted constantly at TMMK facility.

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2.1.2 Project Collaboration with GENERAL MOTORS (G.M.)
This project is collaboration between IMS center and General Motors Technical Center. The
project will aim at implementing IMS tools and methodology for prognostics of vehicle
components. The project is divided into several phases; the first phase of the project focused on
prognostics of wheel speed sensors of the Anti-lock Braking System (ABS); the second phase
of the project is currently dealing with prognostics of the alternator/starter system; later phases
of the project will focus on on-board prognostics.
Phase 1: July 2007 – July 2008 (1 Year)
Phase 2: September 2008 – January 2009 (4 months)
Objective 1: Design a prognostic model to assess the health of the wheel speed sensor and
differentiate sensor faults from system faults.
Objective 2: Design a prognostic model to assess the health of the alternator/starter system.
Objective 3: Study the feasibility of using IMS tools and methodology to perform on-board
prognostics for vehicle components.
For phase I of the project, a thorough study of the existing techniques for sensor
diagnostics/prognostics was conducted through a literature review and a patent search. A
prognostic decision model based on the Watchdog Agent ® was built. The model consists of
two steps: off-line training and on-line testing.
In the step of off-line training, the basic task is to establish the data model for the normal
characteristics based on the features extracted from the direct sensor readings, and train the
artificial intelligence algorithms off-line. For this project two algorithms from the Watchdog Agent
® were used: logistic regression (LR) and statistical pattern recognition (SPR). Data was
initially collected directly from the vehicle’s sensors; then a test-rig was assembled at the IMS
center that allowed inducing faults to sensors and collecting data. Features extracted from the
active wheel speed sensor data are: mean pulse duty factor, mean upper level amplitude, and
mean lower level amplitude. Different features can reflect different physical characteristic of
monitored sensor. Pulse duty factor can reflect the wear or broken of toothed ring, amplitude
related features can reflect the magnetic field change of the sensor. Therefore, changes of
certain feature can be used to indicate the potential faulty element.

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In the step of on-line testing, the real-time sensor readings are captured and the corresponding
features can be extracted, which are further input into the trained model. A CV value can be
obtained to show the current health status. The features are also analyzed and visualized
through SOM. Finally, a comprehensive analysis decision is given, which include the CV value,
the possible physical reason if CV is abruptly changed and corresponding solutions.
For phase II of the project, a test-rig is currently being built at the IMS center to run the
alternator and collect data from both a healthy and faulty alternator. After that a prognostic
model will be developed at the IMS center for the alternator/starter system.
2.1.3 Project Collaboration with HARLEY-DAVIDSON MOTOR COMPANY
(HDMC)
The integration of the Watchdog Agent® prognostics platform and wireless sensor network
successfully realized real-time multiple networked machines health monitoring and prognostics.
It would be directly helping production become more cost effective at Harley-Davidson motor
company as it says in the article “Harley-Davidson: Born to be … predictable” in Lean Tools for
Maintenance & Reliability magazine.
June 2006 – August 2006 (2 Months), June 2007 – August 2007 (2 Months)
c) Objectives:
 Automate the data acquisition without interfering with the machine operation.
 Save the investment of health monitoring and prognosis - upgrade the machine health
monitoring capabilities by using a single platform to monitor multiple machines with
multi-speed and various spindle loads.
 Seamless integration of wireless vibration accelerometer with the Watchdog Agent®
platform.
 Reduce the data analysis time by providing autonomous data processing capabilities
with logistic regression and statistical pattern recognition algorithms.
 Enhance the trend prediction method by providing autoregressive moving average
model (ARMA) and neural network algorithms.
Generate Feature Vector: The feature vector is a signature describing a state of bearing
health, which is formed from features that are considered to be correlated to those that indicate
machine health condition. Features are extracted by Fast Fourier Transform (FFT).
Evaluate Bearing Health: Patterns of the feature vectors that describe up-to-now states of
bearing health are grouped into a health map, using a clustering algorithm such as the method

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of self-organizing map, k-means algorithm, neural networks-based algorithm, and others,
depending on available information about patterns of the feature vectors.
Trend Prediction: Autonomous algorithms (ARMA and Neural Network) are used to predict the
trend of the extracted feature or the trend of the health assessment results (confidence values).
The uniqueness of the technique is the Watchdog Agent platform that provides the health
information of the bearing performance instead of the raw signal data obtained from the
sensors. Utilizing the current signal as a triggering mechanism, the system is able to monitor the
vibration signals within any machining process of the machine without interaction with the
controller. Based on the vibration signals, data processing tools are used to convert data into
health information for the bearing performance. Hence, the information is presented in a radar
chart for visualization. This platform is also designed for integration in the enterprise asset
management systems.
2.1.4 Project Collaboration with KOMATSU
This project was quite different in format, in that a Komatsu engineer spent 1 year at the Center
for Intelligent Maintenance Systems at the University of Cincinnati to understand the algorithms
and data processing methods that were applicable to his application. Based on the guidance
from researchers at the center, the Komatsu engineer would then apply those techniques to the
Komatsu heavy truck diesel engine data collected in the field.
b) Project Objectives:
 Study the feasibility of using IMS tools and methodology to improve the diagnostic and
prognostic monitoring capabilities for the diesel engines used on Komatsu heavy duty
trucks.
 Development of a decision aid tool that can be provided to the engine maintenance
technician.
c) Summary of Technical Approach:
This particular application was for a heavy-duty equipment vehicle used in mining and
construction. The remote prognostics and monitoring system focused on assessing and
predicting the health of the diesel engine component. For this remote monitoring application,
the previously developed architecture for data acquisition and data storage consisted of sending
a daily data set of parameters from the diesel engine to the remote location. The parameters
included pressures, fuel flow rate, temperature, and rotational speed of the engine. These
parameters were taken at key operating points for the engine, such as at idle engine speed or at
maximum exhaust gas temperature. The previously developed architecture was missing the
necessary algorithms to process the data and assess the current health of the engine,
determine the root cause of the anomalous behavior, as well as predict the remaining life of the

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diesel engine. The heavy-duty equipment manufacturer in collaboration with the Center for
Intelligent Maintenance Systems (IMS) developed a systematic approach utilizing several
algorithms from the suite of algorithms in the Watchdog Agent® toolbox to convert the diesel
engine data into health information.
The data preprocessing step consisted of using the Huber method for outlier removal, as well as
the use of an auto-regressing moving average approach to predict a time series value a few
steps ahead to replace missing values. The missing values could be due to an error in the
transmission of the data to the remote location or from an outlier removal preprocessing step.
After preprocessing the data, the next step was to develop a methodology to classify the
different engine patterns in the data to particular engine related problems. The use of a
Bayesian Belief Network (BBN) classification technique used the manufacturer’s experience on
engine related problems along with the pattern history of the data to build the model. This
classification model was able to interpret the anomalous engine behavior in the data and identify
the root cause of the problem at the early stage of degradation.
The last remaining step is the remaining life prediction, and this used a fuzzy logic based
algorithm. The fuzzy membership functions were based on engineering experience as well as
features extracted from the data patterns; this hybrid approach accounts for the uncertainty in
the data and combines data driven and expert knowledge for a more robust approach.
d) Project Deliverables and Results:
An overall visualization of the final output is shown in Figure 8, highlighting the decision aid that
can be provided to the maintenance technician.
Figure 8. Example of Engine Monitoring Interface
2.1.5 Project Collaboration with CATERPILLAR (Phase 1)
The project is subcontracted to the IMS Center as part of Caterpillar’s project called
Manufacturing Asset Capability Information Tools (MACIT). IMS provides consultant and
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technical assistance to Caterpillar. In addition, two PhD students from the IMS Center were
hired as internship by Caterpillar in 2007 and 2008 respectively to help Caterpillar develop their
own technology for machine tool health monitoring.
May 21, 2007 - December 14, 2007 (IMS Researcher Internship)
c) Objectives:
 Develop an autonomous, online machine tool health monitoring system on a CNC lath
test-bed with emphasis on spindle bearing health, tool wear and tool crashes.
 Find the appropriate machine tool health monitoring practices that have good generality
in principle and are quickly duplicable for a variety of machine tools.
The work starts with the common approach for spindle bearing health and tool wear monitoring
based on the previous experience of IMS Center on machine tool prognosis. This approach
implements the data-driven prognostic methodology developed at the IMS center to machine
tool health monitoring, which includes four key steps: data acquisition, signal processing and
feature extraction, health assessment, presentation and reporting. Spindle bearing vibrations,
spindle motor current and feeding motor current are instrumented, whereas controller-
dependent communications and signals are avoided in order to maintain the generality of the
approach. Several algorithms from the Watchdog Agent® toolbox are selected and applied.
Since autonomy is required for the health monitoring system, another key step, called process
alignment and identification, is inserted immediately after data acquisition; the corresponding
technology is developed and validated during the work. This technology enables the health
monitoring system to function well when the machine switches among many jobs frequently, i.e.
in manual or small-batch production.
 A machine tool health monitoring system is built on the test-bed machine.
 The system can run online 24 hours per day and 7 days per week, automatically collecting
data, doing analysis and displaying the machine health conditions for spindle bearing and
tools.
 The system is also capable to manage the raw data and health information in order for
engineers to track the history and find out reasons of critical events, e.g. spindle crashes.
 Several signal streamlining algorithms for process alignment and identification are newly
developed, enabling the whole system to be quickly ported to other type of machines.

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2.1.6 Project Collaboration with CATERPILLAR (Phase 2)
The project is subcontracted to the IMS Center as part of Caterpillar’s project called
Manufacturing Asset Capability Information Tools (MACIT). IMS provides consultancy and
technical assistance to Caterpillar. In addition, two PhD students from the IMS Center were
hired as internship by Caterpillar in 2007 and 2008 respectively to help Caterpillar develop their
own technology for machine tool health monitoring.
May 19, 2008 - August 9, 2008 (IMS Researcher Internship)
c) Objectives:
Validate and automate the machine tool health monitoring system on the CNC lathe test-bed
with emphasis on spindle bearing health, tool wear and tool crashes.
Replicate the prognostic system by developing a machine tool health monitoring system on a
grinding test-bed with emphasis on the grinding wheel and coolant condition.
With the system developed during the first internship, the algorithms are validated by correlating
the features with the actual machining processes. Features are extracted on appropriate
segments of signals that correspond to steady-state (for spindle health) and dynamic state like
in a cutting condition (for tool wear). Furthermore, feature selection for tool wear detection is
aided by knowledge of the known tool life.
With regards to the grinder test-bed, an online data acquisition system has been developed.
Current work is geared towards extracting features from spindle and axis power and current
signals to detect grinding wheel wear.
 Improved machine health monitoring for the lathe test-bed by increasing process
identification accuracy. More relevant features are selected that are more responsive to
tool wear.
 Grinding machine test-bed has been instrumented. An online data acquisition system is
developed.
2.1.7 Project Collaboration with ALSTOM TRANSPORT
IMS has had two training session for ALSTOM Transport personnel during summer 2011; one
for ALSTOM R&D engineers of Train Life Services (TLS) in Barcelona, Spain and one for
ALSTOM R&D engineers in France. The duration of the training sessions was 6 days. The

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training agenda was designed to fit the interests and needs of each group. They were provided
with the summary of each PHM method along with sample examples.
There are currently two feasibility studies being conducted by IMS center in collaboration with
ALSTOM Transport.
1) Degradation Monitoring of the Electro-Mechanical Mechanism in Point
Machines
Point machines are used for operating the railway turnouts and are considered as critical track
elements in railway assets. Failure in the point machine’s mechanism causes delays, increases
railway operating costs and more importantly, causes train accidents. Hence the condition
monitoring and health management of point machines have become a main areas of interest.
This research focuses on establishing a strategy and technical architecture for prognostics and
health management (PHM) of the electro-mechanical point machines. This study has been
conducted on the P80 electro-mechanical point machine data acquired from eight machines in
various locations throughout Italy. Time-stamped data was acquired for point machines current
and voltage signals.
August 2011 – January 2012
c) Project Objectives
The objective of this research is to explore the feasibility of applying a PHM strategy and
technical approach for the condition monitoring of the point machines. Various feature extraction
techniques have been applied to the data. Then, the Principle Component Analysis (PCA) was
applied to the features to assess the health of the machines. There is currently lack of
information about the current and past conditions of the machines and the exact timing of each
participating movement of the mechanism. Despite this, the results obtained show degradation
of the machines and demonstrate the applicability of aforementioned PHM technique for fault
diagnostics and prognostics of point machines.
The technical approach can be summarized as:
 Pre-processing and filtering the raw data
 Data segmentation (four segments in this case) as shown in Figure 9
 Statistical feature extraction and selection
 Health model development using Principal Component Analysis (PCA), as summarized in
Figure 10

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Figure 9. The segmentation of a typical current signal for one operation
Figure 10. Feeding the selected features into PCA and obtaining the health value
The machines work in two different directions; normal and reverse. For machine 1, there have
been three maintenance events for the operation in reverse direction and none for normal
direction. As it can be seen in Figure 11, the health value has an increasing trend in reverse
direction and is almost constant in normal direction which clearly matches with what was
observed in the maintenance events log.

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Figure 11. The obtained health value for machine 1; the green arrows show the times at which
maintenance events occurred.
2) Degradation Monitoring of the Stator Winding Insulation of a 3-Phase
Asynchronous Induction Motor
A locomotive is a railway vehicle that moves/pulls train units along the tracks. Locomotives have
several critical components that are liable to degradation over time, including the bogie
(underlying chassis carrying the wheels) and other internal components such as the motors.
Winding insulation degradation is a key concern in the health management of locomotive
induction motors. This application focuses on establishing a strategy and technical architecture
for prognostics and health management of winding insulation in the 3-phase asynchronous
induction motors.
August 2011 – January 2012
The objective of this research is to explore the feasibility of applying a prognostics and health
management strategy and technical approach for the condition monitoring of the stator winding
insulation of the induction motors with its specific application in the traction motors of high speed
trains manufactured by ALSTOM Transport.

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The technical approach can be summarized as below:
Data segmentation (as shown in left figure of Figure 12)
 Data normalization
 Statistical feature extraction and selection. The extracted features include:
o Peak distance
o Shape f actor
o Crest factor
o Impulse factor
o The standard deviation of each feature extracted from the three currents (three
phases)
Some of the features are plotted in right figure in Figure 12.
Figure 12. (Left) The segmentation of the current signal (blue line is the current for one phase
raw data, and the pink line is the summation of the current for three phases); (Right) Four of the
extracted features from the current signals. As it can be seen, the trends are almost constant.
No trends can be observed in the features as the motor under study is running in healthy
condition. A dataset collected from a fleet of 14 locomotives will be provided by ALSTOM which
will be more suitable for testing different methods and designing a PHM strategy for monitoring
the health of the traction motors.
3) Future Collaborations
 Accelerated life testing of Electronic Module Reliability (EMR)
A project with ALSTOM Transport in France
1 2 3 4 5 6

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 Health monitoring of traction motors for a fleet of 14 locomotives
A project with ALSTOM Transport in Spain
 Bogie’s axle bearing health monitoring
A project with ALSTOM Transport in France
2.1.8 Project Collaboration with PARKER-HANNIFIN
Hydraulic power systems are extensively used in many applications. Hose is the “artery” that
keeps equipment running. Consequences of hose failure are serious. It not only causes
equipment downtime, but also safety issues. Current maintenance scheme is mainly based on
preventive or Fail-and-Fix (FAF). In another word, hydraulic hose are replace by schedule or
when already failed. A higher level of maintenance, Predict-and-Prevent (PAP) is needed to
achieve near-zero down maintenance, which in turn will increase equipment up time and safety.
Sep 1, 2007 - Feb 28, 2009
Figure 13.Smart Hose Vision (Patent)
Figure 13 provides an overview of the Parker Hannifin smart hose vision. The IMS center and
the Nano lab at UC work together toward a smart hose solution by comprising smart sensor
technology and Watchdog® Agent. The smart sensors are able to perform cycle counting,
resistance and strain measurements. It is also able to harvest energy by itself, communicate
wirelessly, and is capable of working under harsh environment. A more advanced smart sensor
should be able to detect failure for threadlike article. The Nano lab at UC is working avidly to
realize such sensors, such as carbon nanotube thread (CNT). Data from sensors is digested in
the Watchdog® Agent for Degradation monitoring, fault detection and classification, and
maintenance scheduling. A project is formulated to achieve this vision.
Hose pulse and bending
Smart Sensory Technology
• Cycle counting
• Energy harvesting
• Working under harsh environment
• Failure detection for threadlike article
Fixture
Pulling
Forces
0
0.2
0.4
0.6
0.8
1
IMS
Watchdog
Agent®
Toolbox
Health
Information
WatchdogAgent®
• Degradation monitoring
• Fault detection and classification
• Maintenance scheduling

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Sensor:
Piezoelectric sensors from Measurement Specialties INC. are selected for hose prognosis. The
sensors are placed in proper locations. Carbon Nanotube Thread (CNT) is under investigation
and design for future hose prognosis.
Testing Condition:
Hose undergo pressurized cycles, until fail.
Testing Procedure:
The voltage change produced by the piezoelectric sensor is recorded by NI-9215 hardware and
LabVIEW software.
Data Analysis:
Watchdog Agent toolbox in Matlab was used for data processing. Each file contains one minute
data. The data analysis process is as follows:
 Signal processing: FFT
 Feature extraction: FFT Frequency Band; Mean; Variance; Peak-to-Peak; Kurtosis;
Peak; damping factors.
 Performance evaluation: Statistical Pattern Recognition
 Sensor fusion: Decision level
e) Deliverables and Results:
 The confidence values (CV) calculated from Peak values, kurtosis and variance have
shown degradation trend before the hose fail. The experiment will be repeated a number
of times under the same condition to validate the result.
 An alternative approach is also conducted to detect hose anomaly when hose is at rest.
The approach has shown promising results to predict hose failure. The method has
advantage in the field because it is working condition independent.
 Two patent ideas are formulated and are under documentation.
2.1.9 Project Collaboration with OMRON
This project will form a value-added collaboration between two participants: the Center for
Intelligent Maintenance Systems (IMS) and the Omron Corporation in Japan. The project will
establish a Precision Energy Management Systems (PEMS) that will aim to reduce energy

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consumptions by linking IMS tools and techniques to Omron products, processes, and business
level.
April 2008 – April 2011 (3 Years)
c) Objectives:
The vision for this project is related to three major players in the Omron infrastructure:
PRODUCT: the PEMS must be able to define, measure, analyze, and predict energy
consumption per product manufactured, in such a way that each product, such as a switch or
sensor, leaving the Omron process line can have an “Energy Label” that resembles the amount
of energy consumed in manufacturing this product. Such an energy label can also be part of the
“Eco-Label” initiative which is an integrated within Omron’s Eco-Product vision.
PROCESS: the PEMS must be able to use the developed energy metrics and parameters for
detailed operational analytics. In other words, the PEMS will assess both equipment-level and
process-level degradation and integrity. Such a correlation between maintenance activities on
the shop floor and energy consumed through the machines and processes is an essential part
of the PEMS initiative and is a large gap in today’s industry operations.
PLANT: the PEMS will enable a strategic IT-centric evolution of Omron’s QCD (Quality, Cost,
Delivery) to consider energy analytics, thus inducing an Eco-IT framework for effective QCDE
development and implementation.
d) Technical Approach:
Figure 14. Technical approach

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2.1.10 Project Collaboration with GENERAL ELECTRIC (GE)
General Electric is planning to develop an Intelligent Prognostic System (IPS) to monitor
machine health using information available in the Computer Numerical Controller (CNC). The
Center for Intelligent Maintenance Systems (IMS), as a collaborator with GE, proposes to
design the offline Prognostic Analysis System (OPAS). The system will periodically conduct
Fixed Cycle Feature Tests, collect data, process the data and conduct offline prognostic
analysis for identifying machine degradation. Finally it should be responsible to notify users of
poor machine health in a clear and intelligent way.
March 2011 – December 2011
Develop an OPAS that can learn the critical factors of machine health and analyze data
collected from online Fix Cycle Feature Test, and report the analysis results.
GE's existing machine testing system periodically conducts fixed cycle tests on all six axes (X,
Y, Z, A and B) of their machine tools, as well as the tool spindles. The MTConnect system
collects data from machine controller signals, such as motor current and position delay, which
are then saved to on offline database. This database is dedicated as a data-repository of the
OPAS and buffers a “snapshot” of the machine every day for analysis. The OPAS proposed by
the IMS center, will draw the FCFT data from the database for the purpose of machine tool
health assessment. The data-sets drawn from the database will undergo pre-processing,
including critical variable identification, segmentation, and feature extraction.
Figure 15. Sensor-less test-bed and FCFT tests

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After pre-processing, exacted data features from identified critical variables will be organized
into the database for every machine. The prognostic analysis will be conducted from two
aspects, anomaly detection and degradation assessment. Firstly, for anomaly detection, we will
target our algorithm development towards candidate abnormal features such as spikes, shifting,
periodical variation and trending. Secondly, algorithms will be developed to recognize normal
conditions of individual machines and to track changes of these normal conditions. Lastly,
suspected degradation will be modeled to quantitatively assess machine health. Analysis results
will be reported by a Human Machine interface (in Matlab) and automatic notification email.
During the analysis, information such as raw data, extracted features, detected anomalies and
suspected degradation with severity index will be stored for manual inspection and historical
record; otherwise the data will be discarded.
Milestone 1 –March 2, 2011: Demonstrate anomaly detection and data visualization capability
Milestone 2 – May 11, 2011: Demonstrate self-learning capability and validate classification
algorithm with different machine normal conditions.
Milestone 3 – August 31, 2011: Demonstrate critical degradation modeling and assessment
Milestone 4 – October 17, 2011: Demonstrate system reporting and notification mechanism
Final project report and presentation – December 28, 2011

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2.1.11 Hong Kong’s Airport Chiller Health Monitoring
Collaborating with Hong Kong Electrical and Mechanical Services Department (EMSD) on the
specific project, IMS designed and employed a systematic method called FCFT (Fixed Cycle
Features Test) to identify the incipient faults of the chiller before it completely shuts down.
Instead of acquiring data continuously, the chiller is run through several possible work
conditions (e.g. 25%, 50%, 75% and 100% load) for two minutes every day and the overall
health condition of different components will be assessed by comparing the readings acquired
during these periods to the baseline.
b) Objectives:
 Identifying the incipient faults of the chiller before it completely shuts down.
 Developing an entire platform that integrates data acquisition, preprocessing, health
assessment and visualization for implementation in the airport.
c) Summary of Technical Approach:
The data acquisition system used in this project consisted of six accelerometers installed on the
housing of 6 bearings on the chiller (Figure 16). In addition, a National Instruments PXI-4472
was used to obtain data from the 6 accelerometers simultaneously. The data logging system
also obtained data from the Johnson Controls OPC (Object-linking-and-embedding for Process
Control) server of the chiller system through an Ethernet connection.
Figure 16. Sensor Installation

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As the working load is subject to change, the proposed method focused on identifying system
behaviors during the transient period between different working loads (Figure 17). Wavelet
Packet Analysis (WPA) method was applied to extract component health related features from
the non-stationery vibration data obtained during these transient period. Gaussian Mixture
Model (GMM) was used as the health assessment model to calculate the confidence value (CV)
of the different components of the chiller. Hence, a CV (using a 0-1 scale, 0 being unacceptable
or faulty and 1 being normal) was derived from both acceleration data and OPC data were
converted into 0 to 1 (0-unacceptable or faulty; 1-normal) information to indicate the health
condition of the chiller components. Finally, a radar chart, as shown in Figure 18, was generated
to show the health condition of all the components, including the shaft, four bearings, the
evaporator, the condenser, the compressor oil and the refrigerant circuit. A drop in confidence
value was displayed close to the center of the radar chart, which indicated an unexpected fault
was likely to happen. The method employed successfully detected the abnormal health
condition of the chiller during validation.
Figure 17. Fixed Cycle Feature Test (FCFT)
Figure 18. Flowchart of Chiller Monitoring System

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d) Project Deliverables and Results:
The entire platform that integrates data acquisition, preprocessing, health assessment and
visualization is compiled into one integrated user interface and is being used by the airport staff.
Example interface is shown in Figure 19.
Figure 19. User Interface for Chiller Health Monitoring
2.1.12 Project Collaboration with TECHSOLVE
The project is subcontracted to the IMS Center as part of the TechSolve program called Smart
Machine Platform Initiative (SMPI). IMS provides consultancy and technical assistance to
TechSolve. In addition, two graduate students from the IMS Center work with TechSolve
engineers to develop a health and maintenance system for a machining center test-bed.
July 2006 - June 2009 (3 Years)
c) Objectives:
 Develop a scalable predictive maintenance system platform to demonstrate self-
assessment and self-prognostics capabilities for machine tool makers and users.

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 Integrate the developed IMS prognostics tools and technologies with machine tool
system in partnership with TechSolve to establish a demonstration system for “Smart
Machine Predictive Maintenance Platform”
 Partner with TechSolve to assist machine tools manufacturers and users to deploy the
validated tools and technologies for accelerated commercialization and leveraged
impacts.
The first major task that was done was to perform a state-of-the-art survey of machine tool
health and maintenance activities and research. The results of this task were used to identify
specific prognostics tasks that need to be addressed: tool unbalance detection, bearing health
monitoring, coolant condition monitoring, axes/ball screw health monitoring and guide
monitoring. The primary result of undertaking such a survey was to be able to identify the overall
goal of the Smart Machine Health and Maintenance System and it is summarized in the figure
below:
Figure 20.Smart Machine Platform
Tool unbalance is a critical most especially in precision machining. Although tool unbalance is
constant regardless of spindle speed, the cutting forces increase with higher spindle speeds
thus producing spindle vibrations. Features are extracted from vibration signals and with the use
of data-driven methods; a confidence value is computed to estimate the state of tool unbalance.
This confidence value is automatically sent to third-party software called Freedom eLogTM. The
implementation overview is depicted in the figure below:

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Figure 21. Smart Machine Platform Implementation
Bearing health monitoring is to be demonstrated using a mock-up rig with actual machine tool
bearings. Faulty bearing data are collected to train a bearing health monitoring system based on
self-organizing map (SOM).
 A state-of-the-art survey was generated to determine the current trend of machine health
monitoring as well as to be aware of other related health and maintenance activities.
 An online monitoring system was developed to detect tool unbalance. It consists of a
data acquisition system, and a health assessment section. Using data driven methods,
the system is able to detect the state of unbalance of a tool assembly (retention knob,
tool holder, cutting tool). Furthermore, the Watchdog Agent® is able to send confidence
values (CV) to Freedom eLogTM.
 A mock-up rig was prototyped as a demonstration test-bed for bearing health monitoring.
Using self-organizing map (SOM), a system was developed to detect different bearing
failure modes.
2.1.13 Intelligent Prognostic Tools for Smart Machine Platform
In order to move towards the first cut correct vision, a methodology and set of intelligent
algorithms are needed to monitor the condition of the machine tool system and components.
This predictive monitoring system can then assess the current and future health state of the
machine and also provide guidance on whether its current state is suitable for producing parts
within the desired specifications.

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June 2009 – June 2011
 To develop a LabVIEW based predictive health monitoring system for machine tools.
 Develop suitable algorithms for predicting the future health state of spindle bearings.
 Develop a health monitoring system for the feed-axis.
 Evaluate a health model that relates tool-holder unbalance to surface quality.
Bearing RUL Modeling
The objective is to develop a set of algorithms that can estimate the health of spindle bearings
and also predict the remaining useful life. The proposed approach monitors the degradation of
the spindle bearings using a self-organizing map algorithm and features extracted from the
vibration, motor current, and temperature signals. The vibration based feature extraction
methods include the use of time domain statistics and peaks in the frequency and envelope
spectrum. The health monitoring algorithm and prediction approach are highlighted in Figure 22.
Figure 22: (a) Health Assessment Algorithm, (b) Prediction Approach
(a) (b)

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0 20 40 60 80 100 120 140 160 180 200
0
5
10
15
20
25
30
35
40
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Operating Time (hours)
HealthValue
Health Trend Value
162 164 166 168 170 172 174 176 178 180
0
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4
6
8
10
12
14
16
18
Operating Time (Hours)
BearingRemainingUsefulLife
Comparing Actual and Estimated Bearing Remaining Useful Life
True RUL
Estimated RUL
Estimated RUL Lower
Estimated RUL Upper
(a) (b)
The health and prediction results using this proposed approach are shown in Figure 23; this is
shown for one of the two run-to-failure data sets from the spindle bearing test-rig. The results
for these two data sets have been encouraging and future work looks to further validate this
method with additional run-to-failure data sets.
Figure 23: (a) Bearing Health Trend, (b) Prediction Results
Feed-Axis Health Monitoring
The feed-axis is a key subsystem on the machine tool which is subjected to a variety of
operating conditions and loads, which over time can lead to the degradation of this particular
subsystem. Also, a loss in performance in a particular axis of the machine tool would have a
detrimental effect on position accuracy and perhaps the quality of the part manufactured. The
proposed approach for monitoring the degradation of the feed-axis is provided in Figure 24.
Figure 24: Feed-axis Health Model Development Flow Chart
Acquirerelevant signals: torque,
position,vibration,temperature, power
Segment and extract features from the
multitudeof monitored signals
Evaluatevariousfeature selection
approachesto obtainthe most robust
feature set for each failure mode
Traina self-organizingmap for each
operatingconditionand failuremode
Use a classificationalgorithm or rule
base system to isolateeach fault
Validatehealthmodelwith seeded faults
on feed-axis test rig
Refinefeature selection model for
improvedresults
Evaluatevarioushealth assessment
algorithms
Evaluateclassificationandrules based
algorithmsfor fault isolation
Developfinal algorithm for real-time
monitoring
Feed-AxisHealth Model Development Feed-AxisHealth Model Validation and Refinement

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0 20 40 60 80 100 120
0
100
200
300
Sample #
MQE
Health Value of Good and Ball Nut Misalignment Fault -Using Nut Misalignment Features (No Load)
0 10 20 30 40 50 60 70 80 90 100
0
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Sample #
MQE
Health Value of Good and Ball Nut Misalignment Fault -Using Nut Misalignment Features (150lb Load)
0 20 40 60 80 100 120
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5
10
15
Sample #
MQE
Health Value of Good and Ball Nut Misalignment Fault -Using Nut Misalignment Features (300lb Load)
For developing the health model in this proposed approach, signals such as the torque, position,
vibration, and temperature are further processed and features are extracted. Furthermore, a
feature selection routine is used to obtain a subset of features that are best at discriminating
between the good and faulty state. These selected features are used as an input into a health
assessment algorithm that quantifies the level of degradation as a function of the deviation from
normal behavior. Lastly, a classification algorithm can be used to isolate the particular fault that
is occurring. After developing this health assessment and fault identification method, a model
refinement and validation step is used to further test the model with additional data sets.
Preliminary results using the proposed approach have been tested with data collected from a
feed-axis test-bed. Figure 25 left figure shows the feed-axis test-rig and Figure 25 right figure
shows the preliminary health assessment results. The preliminary results use data collected
from a healthy system and from a feed-axis system with a misalignment fault of two levels of
severity.
Figure 25. Feed-axis test-rig (Left) and Preliminary health assessment results (Right)
Tool-Holder Unbalance Health and Surface Quality Model
The overall methodology for quantifying the tool holder unbalance condition and then relating
this to work piece surface quality is shown in Figure 6. In the proposed approach, vibration
features related to the fundamental frequency and harmonics are used to train a health
assessment algorithm. As an extension of this health model, a regression or neural network
model is used to relate the surface quality to the measured inputs such as machining
parameters and the tool-holder unbalance condition. Data collected from a machine tool with
different levels of tool-holder unbalance is used to build and validate the health model.

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Figure 26: Tool Holder Unbalance Health and Quality Model Methodology
LabVIEW Interface
A LabVIEW based software tool for key machine tool components or subsystems, such as the
spindle bearings and the feed-axis are part of the scope of this project. An example of the
bearing LabVIEW monitoring program that is currently in development is shown in Figure 7; the
tab shown in this screen capture is for feature plotting. There are respective tabs for feature
extraction, feature selection, health assessment, and prediction as well.
Figure 27: Example of Bearing Health Monitoring and Prediction Software in LabVIEW
e) Project Deliverables:
 LabVIEW based monitoring system configured for monitoring the critical machine tool
components and subsystems.
 Documented report that highlights the methodology and results for each monitored
machine tool component or subsystem.

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2.1.14 Project Collaboration with SIEMENS
This work was proposed to Siemens TTB in early 2008 and is currently in the final stage of IP
agreement negotiation. The work is initially defined to have one student from the IMS Center
working in Siemens TTB for 6 months (subject to change).
2008
The goal is to develop a reconfigurable “Plug-n-Prognose” black box which can be easily and
effectively used for practitioners to assess and predict the performance of the critical
components (bearings, gearboxes, and motors) of the rotary machinery (see figure below).
Visualization tools are provided to present the information of the components’ health which can
be further utilized for precision maintenance decision making.
Figure 28. reconfigurable “Plug-n-Prognose” black box
The innovative idea of the work will be that the product can automatically “sense” different input
signal characteristics which will be linked to the input of the selection tool. The selection tool will
then automatically prioritize the prognostics algorithms which will be used thereafter. After some
pre-configuration, the product can provide accurate prognostics information of rotary machinery
components with minimum human intervention.
The deliverables will be a demonstrable prototype of the autonomic prognostics platform for
rotary machinery. The platform integrates both hardware (PC/104 industrial PC, DAQ board and

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accessories) and software (IMS Watchdog Agent® Toolbox) by developing autonomic tool
selection method, reconfigurable prognostics architecture and a user-friendly interface to enable
remote and embedded health assessment and prediction.
2.1.15 Project Collaboration with Hiwin, Taiwan
a) General background:
The increasing feeding rate and high acceleration value have improved the machine tools
efficiency and accuracy and also reduce the machining times. The each component of the
machine tools can directly affect the overall precision of the machine tools system. The critical
components can be defined based on their criticality level, low failure frequency but high impact.
The condition-based monitoring can enable the manufacturing factories to observe the
degradation of each critical component and calculate their the health value, therefore the overall
health condition of the machine will be evaluated based on the health condition of each
component. This health indicator like health value, confidence value can provide and predict the
information of the machine’s health condition. So the predictive analytical algorithms provide
significant improvements of adding PHM to traditional maintenance schemes. Machine data is
effectively transformed by PHM algorithms into valuable information that can be used by factory
managers to optimize production planning, save maintenance cost and minimize equipment
downtime.
The ball screw is the one of the most important elements for industrial machinery and precision
machining. Its primary feature is to convert rotary motion to linear motion with high accuracy,
reversibility and efficiency. However the ball screw performance degrades over time after
repeated usage, which can impact the feed-axis system and undesirable loss of position
accuracy. Thus, the accuracy of ball screw is the prior concern based on the quality requirement
of high precision performance machines. By acquiring information of the current health state of
the ball screw and its degradation trend, it will save costs in maintenance and avoid products
with low quality and downtime due to unexpected ball screw failures if proactive maintenance
can be scheduled before the machines loss accuracy, or even failure happens.
b) Project duration:
Phase I: January, 2011 ~ July, 2012
Phase II: January, 2013 ~ March, 2014
c) Project objectives:
This research focused on further establishing a strategy and technical architecture for
prognostics and health management (PHM) of ball screw. By analyzing the vibration data and
temperature data acquired from the different location on the ball screw, this study’s target is to
develop and apply a PHM strategy and technical approach for early detection of failure and
Remaining-useful life prediction. Frequency and time domain features are extracted to evaluate
the degradation. Neural Network (NN) is applied to map the information gained from external

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sensors with position loss. So far, the position model is established using data collected from
one test and validated from another one. The method showed reasonable results, but it also
needs more testing data to refine the model and increase the prediction accuracy.
In order to understand the ball screw’s degradation process, experiments have been conducted
in both the Hiwin factory and also the IMS center lab. Specific highly stress tests have been
designed to accelerate the ball screw’s degradation. Learning from both sensor-less and
sensor-rich fault diagnosis tests in 2011, it is quite obvious that the external sensors can collect
more information and directly target the failure modes and location when compared with only
motor controller data. Thus, extra sensors like accelerometers and thermocouples are installed
on certain key locations to monitor its performance degradation. At the same time, the linear
encoder is used to measure the position loss in order to validate and benchmark the health
monitoring methods.
The overall methodology is developed under Matlab® environment. For the ball screw case, it
has its own specific movement pattern, so a systematic method is tailored to analyze the ball
screw data, as seen in Figure 29.
Figure 29. Ball Screw Health Monitoring System Flowchart
In the smart factory, all the physical entities in the factory are connected together physically and
virtually. In the physical space, the sensors are utilized to measure and detect the occurrence of
Ball Screw Health Assessment
and Fault Diagnosis
Set up Data Acquisi on and
Signal Set
Data Preprocessing
Data Quality
Check
Select Tools for health
assessment and fault diagnosis
!
N
Y
Preliminary Analysis
Outlier
Detec on
N Y
Diagnose Degrada on
Pa ern
Failure Mode Iden fica on and
Evalua on
Life Degrada on
Predic on
RUL Predic on (Posi on
Error Es ma on)
stop
N
Y

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a fault or anomaly, and isolate the location of the fault and identify the failure types. Beside the
sensor signals, the outputs from the machine controller like current, voltage, power consumption
can also be used to reflect the machines’ overall health degradation. These physical entities are
connected in the cyber space through Internet. The sensor signals are collected through data
acquisition hardware, and either stored locally or uploaded to cloud server. After storing the
data, pre-processing techniques are employed to check the data quality and increase the
signals’ signal-to-noise ratio (SNR), such as the data filtering, quantization, etc. Data
segmentation is used to select the interesting segment from the input signals. Furthermore, the
features (health indicators) are extracted, and health value is calculated over time to evaluate
the behavior of the machine components and detect incipient signs of failure even before the
actual failure event. Beside of this data-driven method, the physical model and experiment
knowledge are also applied to fully understand the performance of these components, like the
FEA model, mode analysis, etc. In the cyber space, all of these models are integrated to convert
the data to information, with which the customers can schedule the timely maintenance before
the failure.
In order to model the components’ behavior quickly and effectively, the accelerated degradation
test in the lab is utilized in the machine reliability analysis, the different stress factors are
screened and the important stress factors are picked according to the quantization matrix. After
that, the characteristics tests are conducted to identify the initial condition. Thus the data
collected from multiple degradation process under different stress levels can generate the
degradation pattern for the component, thus the time conversion between these different
degradation pattern will be validated, which can assist to identify the degradation trend in the
actual industrial application. After establishing the degradation model, this PHM model will be
integrated in the cyber space. The data collected from the machine components will be
processed through this PHM model and thus a virtual component is modeled in the cyber space,
so it can simulate and predict the health pattern of the machine component in the reality.

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Figure 30.Smart Machine Component Prognostics Scheme
2.1.16 Project with INSTITUTE FOR INFORMATION INDUSTRY (III), TAIWAN
a) General background:
With the growing maturity of the Information and Communications Technology (ICT), machining
and manufacturing sectors have been accelerating the pace to integrate with the related ICT
techniques, so as to enhance the competence among global competitors. Since the number of
machines being monitored increases, the volume of the data being collected at every level of
machines/manufacturing lines/factories to be transferred and processed becomes so enormous
that the conventional techniques need to be improved. Also, due to differences between
machining processes for different products, the parameters and physical characteristics that
need to be monitored vary from case to case. Therefore, in order to efficiently collecting
diagnostic information, a dynamic feature extraction system is to be designed and integrated
with the current machine tool monitoring platform. This upgrade of the software will enable the
data collection system to dynamically select the useful parameters related to machine health,
and enlarge expert system library to alleviate the burden of communications and increase the
efficiency of value-added ICT service.
Physical Space
Cyber Space
Cyber Space:
• Ball Screw Time
Machine
• Ball Screw Clusters
• Health Evalua on
and Life Predic on

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Figure 31. Framework of dynamic condition-based feature extraction
b) Project duration:
April, 2013 ~ November, 2013
c) Project objectives:
The developed real-time dynamic feature extraction approach is to be integrated with the
intelligent machine tool DAQ platform “ServBox”, which is a product of Institute for Information
Industry. The objectives of this research include:
 Build a knowledge library of mechanical component health diagnosis that consists of (1)
a summary features to be extracted, (2) relations between features and effectiveness of
features for diagnosis and, and (3) quantified evaluation of the effectiveness;
 Design a hierarchical structure for dynamic feature extraction.
d) Summary of technical approach:
The system includes 3 main function units:
1) A knowledge base for critical machine components, their failure modes, and related data
resources/features. It is constructed through expertise interviewing and literature survey and
based on quality function deployment (QFD).
2) A decision tree for feature priority will be constructed automatically based on Bayesian
probability learning from machine condition and prior knowledge. The execution flow defines
Data acquisi on device

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most critical features at the first level (so called Pivot Features) and secondary features in lower
levels.
3) In routine monitoring tasks, under normal conditions, DAQ only calculates Pivot Features and
analyzes them to make initial judgments of whether anomaly happens. If any abnormal
condition is detected, DAQ will further go through lower levels, calculate and evaluate
secondary features, and eventually infer root cause of the problem.
By incorporating the proposed mechanism, DAQ will be able to operate with minimal
computational resource under normal conditions. The overall procedure is presented in the
figure below. Besides, the PHM system can react and perform diagnosis promptly when
anomaly happens. With such improvement, the future DAQ and PHM system together will be
able to perform real-time health diagnosis and prognosis in a more bandwidth and
computational efficient way.

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Figure 32. Overall approach flowchart for dynamic feature extraction
e) Project deliverables and results
Project deliverables:
 Library of mechanical component health diagnosis, including feature extraction
algorithms, and quality function deployment (QFD) table;
Component(Sensor(Feature/Table/
Sensors/ Controller/ Vibra5on/ Power/ …/
Current/ Posi5on/
Features/ Mean/ Accuracy/Variance/RMS/ Kurtosis/Sha@/freq.//Bearing/freq./Mean/Max/ …/
Components/&/
Problems/
Spindle/ L/ NA/ NA/ M/ L/ H/ NA/ L/ L/
…/
Bearing/ L/ NA/ NA/ M/ M/ L/ H/ L/ L/
Machine/Tool/ M/ NA/ NA/ M/ M/ M/ NA/ H/ H/
Tool/Holder/ NA/ NA/ NA/ M/ M/ M/ NA/ NA/ NA/
Feed/Axis/ L/ M/ M/ M/ L/ M/ NA/ L/ L/
Servo/Motor/ H/ L/ NA/ L/ L/ L/ NA/ H/ H/
Comp./Power/ Low/ Medium/ Medium/ Medium/Medium/ High/ High/ Low/ Low/
…/Comp./Time/ Medium/Medium/ Medium/ Medium/Medium/ High/ High/ Low/ Low/
Storage/ Low/ Medium/ Low/ Low/ Low/ Low/ Medium/ Low/ Low/
H (high, or strong), M (medium) or L (low, or weak) and NA (not applicable)
Score/values:/NA/=/0,/L/=/1,/M/=/3,/H/=/9/
Identify critical
problems and related
features
Construct
Component-Sensor-
Feature table
• Spindle
• Bearing
• Machine
Tool
• Tool
Holder
• Feed
Axis
• Servo
Motor
• Current
average
• Vibration
RMS
• Power
average
• Frequency
Amp.
• …
Problems / Components Related Features
First layer construction
Second layer
construction
Human machine
Interface integration
!
» By incorporating the proposed mechanism, DAQ will be able to
operate with minimal computational resource under normal
conditions.
» The PHM system can react and perform diagnosis promptly when
anomaly happens.

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 Feature extraction hierarchical levels and the algorithm to build them;
 A published research article.
Results:
The gearbox data from the PHM 2009 Data Challenge were used for method validation. The
model has achieved 100% accuracy for health assessment, 100% accuracy for gear and
bearing fault diagnosis, and 100% detection rate (with 12% false alarms) for shaft fault
diagnosis. In terms of data processing speed, the algorithms consumed on average 0.24
seconds to process 1.5 length of data under healthy conditions, and 0.92 seconds under faulty
conditions when diagnostic feature extraction was triggered. The testing results well
demonstrated the capability of the proposed method in optimization of computational recourses
while maintaining fault classification accuracy.
Table 1. Sets of experiment with different health conditions used for model training and testing
Figure 33 Health assessment and fault diagnosis result using dynamic feature extraction
0 100 200 300 400
0
0.2
0.4
0.6
0.8
1
HealthValue(CV)
Time(s)
0 100 200 300 400
0
0.2
0.4
0.6
0.8
1
ProbabilityofGearFault
Time(s)
0 100 200 300 400
0
0.2
0.4
0.6
0.8
1
ProbabilityofBearingFault
Time(s)
0 100 200 300 400
0
0.2
0.4
0.6
0.8
1
ProbabilityofShaftFault
Time(s)
Baseline 1
Chipped Gear&Eccentric Gear
Ecentric Gear
Baseline 2
Broken Gear&Ball defect Bearing
Inner Race Defect&Bad Keyway
Baseline 3
Outter Race Defect&Imbalance Shaft

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2.1.17 Project Collaboration with FMTC
This project was a collaborative work between IMS center and FMTC on developing novel
winding fault detection techniques in 3-phase induction motors. Among the different faults that
may develop in an induction motor, winding faults are the second most occurring faults after
bearing faults. Winding faults can cause severe damage to the induction motors if they
progress. However, if detected early, they can be fixed with minimal cost and a major downtime
and repair cost can be avoided. Over the course of this project, IMS center worked with FMTC
and successfully developed early winding fault detection techniques based on current, voltage
and vibration signals.
June 2013 – June 2014
Objective 1: Development of an early winding fault detection technique based on current and
voltage signals
Objective 2: Development of an early winding fault detection technique based on vibration
signals
FMTC designed and built a test-bed consisting of an induction motor, a variable frequency drive
and a magnetic brake for applying load on the motor. Winding faults of different types and
different levels of severity were induced in the motor using shorted circuits and a variable
resistor. Current and voltage signals at three phases were collected along with vibration and
motor speed signals.
For analyzing the current and voltage signals, a signal processing technique was developed to
preprocess the signals and compensate the effect of the variable frequency drive on the signals.
Afterwards, three different techniques including negative-sequence impedance, voltage
mismatch approach and Park’s vector approach were tested on the data. The results from the
three techniques were consistent and successfully detected the incipient winding anomalies in
the induction motor.
For analyzing the vibration signals, a signal enhancement technique based on kurtogram was
developed. After enhancing the signals using Time Synchronous Averaging (TSA) and retaining
the frequency band of interest, envelope analysis was applied to detect the winding fault related
impacts in the vibration signature. The results were consistent with the results obtained from
motor current and voltage signals and proved to be capable of detecting winding faults in early
stages.
The results from this project were presented in two international conferences.

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2.1.18 Project Collaboration with Korea Aerospace University (KAU)
This project was a collaborative work between IMS center and KAU for developing a
Prognostics and Health Assessment (PHM) framework and algorithm for material handling
systems. Material handling systems consist of a multiplicity of components used for storage,
retrieval and handling of goods and equipment. The objective of this project was to monitor their
critical components to reduce the costs of downtime and maintenance and improve the
productivity of such systems.
Phase 1 (Survey): July. 2014 – Dec. 2014 (6 months)
Phase 2 (PHM System Development): March 2015 – Aug. 2015 (6 months)
Objective 1: A survey paper and a report on the PHM methodologies for critical components of
material handling systems
Objective 2: Development of PHM systems for critical components of material handling systems
Objective 3: Development of a Cyber-Physical framework for material handling systems
During the first phase of the project, a comprehensive survey was performed on the health
assessment and monitoring of the critical components and sub-systems of material handling
systems including conveyor systems and shuttle battery. The paper was published in the
International Journal of Advanced Logistics.
For the second phase, IMS center and KAU are collaborating to develop methodologies and
algorithms for PHM of material handling systems. A test-bed was setup for testing the batteries
used for shuttle and experiments were performed on the batteries during various
charge/discharge regimes to develop a model for the degradation of the batteries. Currently, the
tests are still ongoing and IMS is working to develop a degradation model for batteries to
estimate their remaining useful life. KAU is designing a test-bed for acquiring data from other
critical components of typical material handling systems. IMS center will be analyzing the data
to build degradation models for such components and sub-systems. Moreover, IMS center will
develop a framework for building a Cyber-Physical System (CPS) for material handling systems.
2.1.19 Project Collaboration with Cosen
Machine Tool Condition Monitoring (TCM) is an effective way for achieving optimal utilization of
machine tools, with which the best time for replacing a worn-out tool can be determined by

IMS Project Portfolio - 2015

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