Emergence of uniquely addressable embeddable devices has raised the bar on Telematics capabilities.
Though the technology itself is not new, its application has been quite limited until now. Sensor based
telematics technologies generate volumes of data that are orders of magnitude larger than what operators
have dealt with previously. Real-time big data computation capabilities have opened the flood gates for
creating new predictive analytics capabilities into an otherwise simple data log systems, enabling real-time
control and monitoring to take preventive action in case of any anomalies. Condition-based-maintenance,
usage-based-insurance, smart metering and demand-based load generation etc. are some of the predictive analytics use cases for Telematics. This paper presents the approach of condition-based maintenance using
real-time sensor monitoring, Telematics and predictive data analytics.
CBM Cost Benefit Analysis by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews some of the elements of CBM Cost Benefit Analysis. The analysis consider implementation and non recurring engineering cost as well as deferred, eliminated scheduled maintenance, reduced unscheduled maintenance, and operational cost savings drivers. Specific examples from aircraft, ground vehicle, and industrial applications are provided.
#phmdesign
https://phmdesign.com
CBM Requirements by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews:
Conceptual functional architecture:
Describes functions and functional interactions
Traces functions to capabilities or services desired in the COO
Conceptual physical architecture:
Allocates and describes the conceptual implementation of functions
Traces implementation to function
Activity Flows:
Identifies primary paths through the principal use-cases to meet the goals and interests of the stakeholders
Trades identify preferred path which, in turn, provides context for requirements derivation and operational thread development.
#phmdesign
https://phmdesign.com
Embedded Condition Based Maintenance A New Modeling Approach iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Beyond Predictive and Preventive MaintenanceHarshad Shah
Did you know you could save up to 18% with a Predictive Maintenance Management Strategy?
Predictive and Preventive Maintenance is a well-known term and most know what it entails but have you looked past the strategy checklist? Here is for a more in-depth look beyond Predictive and Preventive Maintenance.
One of the major challenges for Gas Turbine users is to ensure high level of engine availability and reliability, and efficient operation during their complete life-cycle. For this purpose, Various maintenance approaches have been introduced over the years for the gas turbine maintenance: Breakdown Maintenance or Run to Failure, Preventive Maintenance or Scheduled Maintenance and Condition-Based Maintenance (CBM). Here the focus is on CBM or predictive maintenance.
CBM Cost Benefit Analysis by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews some of the elements of CBM Cost Benefit Analysis. The analysis consider implementation and non recurring engineering cost as well as deferred, eliminated scheduled maintenance, reduced unscheduled maintenance, and operational cost savings drivers. Specific examples from aircraft, ground vehicle, and industrial applications are provided.
#phmdesign
https://phmdesign.com
CBM Requirements by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews:
Conceptual functional architecture:
Describes functions and functional interactions
Traces functions to capabilities or services desired in the COO
Conceptual physical architecture:
Allocates and describes the conceptual implementation of functions
Traces implementation to function
Activity Flows:
Identifies primary paths through the principal use-cases to meet the goals and interests of the stakeholders
Trades identify preferred path which, in turn, provides context for requirements derivation and operational thread development.
#phmdesign
https://phmdesign.com
Embedded Condition Based Maintenance A New Modeling Approach iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Beyond Predictive and Preventive MaintenanceHarshad Shah
Did you know you could save up to 18% with a Predictive Maintenance Management Strategy?
Predictive and Preventive Maintenance is a well-known term and most know what it entails but have you looked past the strategy checklist? Here is for a more in-depth look beyond Predictive and Preventive Maintenance.
One of the major challenges for Gas Turbine users is to ensure high level of engine availability and reliability, and efficient operation during their complete life-cycle. For this purpose, Various maintenance approaches have been introduced over the years for the gas turbine maintenance: Breakdown Maintenance or Run to Failure, Preventive Maintenance or Scheduled Maintenance and Condition-Based Maintenance (CBM). Here the focus is on CBM or predictive maintenance.
The Predictive Maintenance solution accelerator is an end-to-end solution for a business scenario that predicts the point at which a failure is likely to occur. Use this solution accelerator proactively to optimize maintenance and to create automatic alerts and actions for remote diagnostics, maintenance requests, and other workflows. The solution combines key Azure IoT services like IoT Hub and Stream analytic.
Carl Byington has over thirty years developing models and analyzing data from various equipment and critical assets. He is currently a consultant who leads PHM Design, LLC, based in the greater Atlanta area. One operational strategy Carl Byington employs involves condition-based maintenance (CBM). This protocol rests on the concept that equipment failure is a process, rather than one single event.
Condition-based maintenance (CBM) is a philosophy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. By contrast, time-based or use-based maintenance involves performing periodic maintenance after specified periods of time or hours of operation. CBM has the potential to decrease life-cycle maintenance costs (by reducing unnecessary maintenance actions and greater operational failures), increase operational readiness, and improve safety.
Implementation of condition-based maintenance involves predictive diagnostics (i.e., diagnosing the current state or health of a machine and predicting time to failure based on an assumed model of anticipated use). CBM and predictive diagnostics depend on multisensor data—such as vibration, temperature, pressure, and presence of oil debris—which must be effectively fused to determine machinery health.
Why predictive maintenance should be a combined effortWouter Verbeek
Predictive maintenance is an extremely promising maintenance strategy, but implementation often turns out to be way more complicated than expected. A lot of attempts to implement predictive maintenance strand at the same departments as where they were initiated. The key towards successful implementation of predictive maintenance is to combine the knowledge of all departments in making decisions. In this presentation we start by explaining, based on the subject of sensor selection, why involving your entire organization is so important. Afterwards we give advice on how to implement predictive maintenance, give examples based on the Strukton Worksphere case and discuss how to get your entire organization on board.
Incorporating a predictive maintenance strategy has been proven to provide savings. Learn how to use your pressure and temperature gauges as part of your initiatives.
Predictive Maintenance vs Preventive MaintenanceMobility Work
Whether your business is small, medium or large, effective equipment maintenance is crucial. The CMMS market is flooded with solutions promising cost and time savings but also demanding a solid investment. Every maintenance professional will tell you that predictive and preventive maintenance are absolutely worth it and of highest importance for asset management.
But how to choose the right maintenance strategy for your equipment according to your budget?
How to switch from reactive maintenance to preventive maintenance complete ...BryanLimble
To show you that the switch from reactive to preventive maintenance doesn’t have to be hard, complicated, and expensive, we devised this comprehensive step-by-step guide that will guide you through the whole process.
Condition-Based Maintenance Basics by Carl Byington - PHM Design, LLCCarl Byington
Condition-based maintenance (CBM or CBM+) is a strategy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. CBM is enabled by the evolution of key technologies, including improvements in - sensors, microprocessors, digital signal processing, simulation modeling, multisensor data fusion, reliability engineering, Internet of Things (IoT) connectivity, data warehousing, cloud computing, machine learning (ML), artificial intelligence (AI), and predictive analytics. CBM involves monitoring the health or performance of a component or system and performing maintenance based on that inferred health and in some cases, predicted remaining useful life (RUL). This predictive maintenance philosophy contrasts with earlier ideologies, such as corrective maintenance — in which action is taken after a component or system fails — and preventive maintenance — which is based on event or time milestones. Each involves a cost tradeoff.
Carl Byington with PHM Design, LLC reviews some of the elements of CBM.
#phmdesign
https://phmdesign.com
Definition of Preventive Maintenance, PM Elements, Plant Characteristics In Need of a PM Program, Principle for Selecting Items for PM, PM Measures, PM Models with examples
Definition, types of corrective maintenance, steps and cycle;
Measures of corrective maintenance are: Mean Corrective Maintenance Time , Median Active Corrective Maintenance Time, Maximum Active Corrective Maintenance Time.
Then different models : a system that can either be in up (operating) or down (failed) state; a system that can either be operating normally or failed in two mutually exclusive failure modes; a system that can either be operating normally, operating in degradation mode, or failed completely; a two identical-unit redundant (parallel) system. At least one unit must operate normally for system success.
EGT10 DESIGN AND APPLICATION FOR POSITION GPS TRACKER WITH VISUAL BASICijmnct
As a navigation tool, GPS can be used to guide a person towards the desired location. The trick is to
combine data obtained from the GPS coordinates with a digital map. From there it can be known to the
person's position relative to its target location. This study tries to view a map using visual basic. Data from
a map using GPS data EGT-10. The results showed that the data received by GPS coordinates EG T-10 the
same as the Garmin GPS 60, while the decimal value of the minute differences are influenced by the level
of accuracy of each GPS and within the accuracy of GPS is used. GPS can be visualized on a digital map.
TriBA(Triplet Based Architecture) is a Network on Chip processor(NoC) architecture which merges the
core philosophy of Object Oriented Design with the hardware design of multicore processors[1].We
present TriBASim in this paper, a NoC simulator specifically designed for TriBA.In TriBA ,nodes are
connected in recursive triplets .TriBA network topology performance analysis have been carried out from
different perspectives [2] and routing algorithms have been developed [3][4] but the architecture still lacks
a simulator that the researcher can use to run simple and fast behavioural analysis on the architecture
based on common parameters in the Network On Chip arena. TriBASim is introduced in this paper ,a
simulator for TriBA ,based on systemc[6] .TriBASim will lessen the burden on researchers on TriBA ,by
giving them something to just plug in desired parameters and have nodes and topology set up ready for
analysis.
The Predictive Maintenance solution accelerator is an end-to-end solution for a business scenario that predicts the point at which a failure is likely to occur. Use this solution accelerator proactively to optimize maintenance and to create automatic alerts and actions for remote diagnostics, maintenance requests, and other workflows. The solution combines key Azure IoT services like IoT Hub and Stream analytic.
Carl Byington has over thirty years developing models and analyzing data from various equipment and critical assets. He is currently a consultant who leads PHM Design, LLC, based in the greater Atlanta area. One operational strategy Carl Byington employs involves condition-based maintenance (CBM). This protocol rests on the concept that equipment failure is a process, rather than one single event.
Condition-based maintenance (CBM) is a philosophy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. By contrast, time-based or use-based maintenance involves performing periodic maintenance after specified periods of time or hours of operation. CBM has the potential to decrease life-cycle maintenance costs (by reducing unnecessary maintenance actions and greater operational failures), increase operational readiness, and improve safety.
Implementation of condition-based maintenance involves predictive diagnostics (i.e., diagnosing the current state or health of a machine and predicting time to failure based on an assumed model of anticipated use). CBM and predictive diagnostics depend on multisensor data—such as vibration, temperature, pressure, and presence of oil debris—which must be effectively fused to determine machinery health.
Why predictive maintenance should be a combined effortWouter Verbeek
Predictive maintenance is an extremely promising maintenance strategy, but implementation often turns out to be way more complicated than expected. A lot of attempts to implement predictive maintenance strand at the same departments as where they were initiated. The key towards successful implementation of predictive maintenance is to combine the knowledge of all departments in making decisions. In this presentation we start by explaining, based on the subject of sensor selection, why involving your entire organization is so important. Afterwards we give advice on how to implement predictive maintenance, give examples based on the Strukton Worksphere case and discuss how to get your entire organization on board.
Incorporating a predictive maintenance strategy has been proven to provide savings. Learn how to use your pressure and temperature gauges as part of your initiatives.
Predictive Maintenance vs Preventive MaintenanceMobility Work
Whether your business is small, medium or large, effective equipment maintenance is crucial. The CMMS market is flooded with solutions promising cost and time savings but also demanding a solid investment. Every maintenance professional will tell you that predictive and preventive maintenance are absolutely worth it and of highest importance for asset management.
But how to choose the right maintenance strategy for your equipment according to your budget?
How to switch from reactive maintenance to preventive maintenance complete ...BryanLimble
To show you that the switch from reactive to preventive maintenance doesn’t have to be hard, complicated, and expensive, we devised this comprehensive step-by-step guide that will guide you through the whole process.
Condition-Based Maintenance Basics by Carl Byington - PHM Design, LLCCarl Byington
Condition-based maintenance (CBM or CBM+) is a strategy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. CBM is enabled by the evolution of key technologies, including improvements in - sensors, microprocessors, digital signal processing, simulation modeling, multisensor data fusion, reliability engineering, Internet of Things (IoT) connectivity, data warehousing, cloud computing, machine learning (ML), artificial intelligence (AI), and predictive analytics. CBM involves monitoring the health or performance of a component or system and performing maintenance based on that inferred health and in some cases, predicted remaining useful life (RUL). This predictive maintenance philosophy contrasts with earlier ideologies, such as corrective maintenance — in which action is taken after a component or system fails — and preventive maintenance — which is based on event or time milestones. Each involves a cost tradeoff.
Carl Byington with PHM Design, LLC reviews some of the elements of CBM.
#phmdesign
https://phmdesign.com
Definition of Preventive Maintenance, PM Elements, Plant Characteristics In Need of a PM Program, Principle for Selecting Items for PM, PM Measures, PM Models with examples
Definition, types of corrective maintenance, steps and cycle;
Measures of corrective maintenance are: Mean Corrective Maintenance Time , Median Active Corrective Maintenance Time, Maximum Active Corrective Maintenance Time.
Then different models : a system that can either be in up (operating) or down (failed) state; a system that can either be operating normally or failed in two mutually exclusive failure modes; a system that can either be operating normally, operating in degradation mode, or failed completely; a two identical-unit redundant (parallel) system. At least one unit must operate normally for system success.
EGT10 DESIGN AND APPLICATION FOR POSITION GPS TRACKER WITH VISUAL BASICijmnct
As a navigation tool, GPS can be used to guide a person towards the desired location. The trick is to
combine data obtained from the GPS coordinates with a digital map. From there it can be known to the
person's position relative to its target location. This study tries to view a map using visual basic. Data from
a map using GPS data EGT-10. The results showed that the data received by GPS coordinates EG T-10 the
same as the Garmin GPS 60, while the decimal value of the minute differences are influenced by the level
of accuracy of each GPS and within the accuracy of GPS is used. GPS can be visualized on a digital map.
TriBA(Triplet Based Architecture) is a Network on Chip processor(NoC) architecture which merges the
core philosophy of Object Oriented Design with the hardware design of multicore processors[1].We
present TriBASim in this paper, a NoC simulator specifically designed for TriBA.In TriBA ,nodes are
connected in recursive triplets .TriBA network topology performance analysis have been carried out from
different perspectives [2] and routing algorithms have been developed [3][4] but the architecture still lacks
a simulator that the researcher can use to run simple and fast behavioural analysis on the architecture
based on common parameters in the Network On Chip arena. TriBASim is introduced in this paper ,a
simulator for TriBA ,based on systemc[6] .TriBASim will lessen the burden on researchers on TriBA ,by
giving them something to just plug in desired parameters and have nodes and topology set up ready for
analysis.
TRUST BASED SECURITY MODEL TO WITHSTAND AGAINST BLACK HOLE AND GREY HOLE ATTA...ijmnct
Significant features of Mobile Ad Hoc Networks make it suitable for modern military communication. These
characteristics also weaken the security aspects in terms of attacks. Among the attacks, black hole and grey
hole attacks are notable since they are launched internally and cannot be identified easily. The military
communication requires confidential information sharing, ensuring correct identity of soldiers but offering
such facility in military based MANET is difficult due to its unique nature. As providing authentication is a
first form of security, in this article we propose a trust based security model to identifying black hole and
grey hole nodes that weaken or collapse the success of mission in military communication. To provide
authentication, we incorporate soldier’s interpersonal characteristics in terms of Stereo trust, Situational
Awareness trust and soldier’s operational things in terms of first-hand information, second hand
information and soldier’s current trust. Simulation results show the efficiency of our proposed model in
terms of identifying black hole and grey hole nodes and its performances are compared with an existing
model.
Design and Implementation a New Energy Efficient Clustering Algorithm Using t...ijmnct
Wireless Sensor Networks are consist of small battery powered devices with limited energy resources.Once
deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy
source is not feasible. Hence, one of the most important issues that need to be enhanced in order to
improve the life span of the network is energy efficiency. to overcome this demerit many research have
been done. The clustering is the one of the representative approaches. In this paper, we introduce a
dynamic clustering algorithm using Fuzzy Logic and genetic algorithm. In fact, using fuzzy system design
and system optimization by genetic algorithm is presented approach to select the best cluster head in
sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented
in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in
comparison with LEACH method is calculated in select the cluster head. The results of evaluations is
representative of a reduction the MSE metric in proposed method in comparison with LEACH method for
select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless
sensor network and can cause the reduce energy consumption and increase network lifetime.
A gateway based energy efficient multi hop routing protocol for wireless sensor networks (WSNs) is
introduced. The main aim of our paper is to design a protocol which minimizes energy consumption.
Gateway nodes are deployed in sensing field.
A highway variable speed limit control system based on internet of thingsijmnct
It is an important issue of Intelligent Transportation System, how to quickly relieve the highway congestion.
The variable speed limit control system can limit the traffic flow before the highway congestion zone. So it
is wildly used to relieve the highway congestion. In this paper, we propose a novel highway variable speed
limit control system base on the Winternet Architecture. We use the STeC language to describe the system’s
formal model. Finally, we give control algorithm of the highway variable speed limit control system, which
based on the formal model. The simulation results show the control algorithm is effective.
ENERGY USAGE SOLUTION OF OLSR IN DIFFERENT ENVIRONMENTijmnct
One of the main issues in deploying the MANET scenario using simulation tools is how to adapt theMANET protocol setting to network scenarios such as the suitable number of mobile devices (number ofnodes), network space area, data transmission rate, and the node mobility speed. This issue of adaptionrepresents an important focus of research because, thus far, MANET protocol only works with fixed
network settings, which may not be suitable in certain network scenarios. Premised on this critical
situation, the main objective of this research is to implement the simulation scenario for the Ad hoc network
using OLSR routing protocol. Once implemented, the energy usage and node keep alive of this Ad hoc
network were tested and analyzed
A Hybrid PAPR Reduction Scheme for OFDM System ijmnct
Orthogonal frequency division multiplexing (OFDM) i
s considered as most efficient technique for future
wireless communication systems due to its higher sp
ectral bandwidth efficiency, robustness to frequenc
y
selective fading channels, etc. However, the succes
sful implementation of the OFDM system necessitates
several difficulties. The biggest disadvantage to w
ork with OFDM system is its high peak-to-average po
wer
ratio PAPR leadsto severe inter carrier interferenc
e, out-of-band radiation, and poor bit error rate
performance due to the nonlinearity of the high pow
er amplifier. In this paper, a novel hybrid techniq
ue is
proposed to reduce PAPR further and comparison has
been done with conventional techniques as well.
Simulated results are presentedconfirm theoretical
results.MATLAB 7.5 is used to simulate the results
for system parametersconsidered.
A review paper on the papr analysis of orthogonal frequency division multiple...ijmnct
OFDM (Orthogonal Frequency Division Multiplexing) has been raised a new modulation technique. Due
to its advantages in multipath fading channel e.g. robust against ISI, ICI and some other advantages like
best QoS for multiple users, efficient usage of bandwidth it is suggested to be the modulation technique for
next generation 4G networks e.g. LTE. But along with all its advantages there are some disadvantages also
e.g. High PAPR (Peak to Average Power Ratio) at the transmitter end and BER (Bit Error Rate) at the
receiving end. Since OFDM is only used in the downlink of 4G networks. To reduce the problems of OFDM
some techniques e.g. SLM, PTS, Clipping, Coding, & Pre-coding etc are suggested but none of them is
reduce the PAPR and BER to an acceptable value. This Paper will discuss some techniques of PAPR &
BER reduction, and their advantages and disadvantages in detail.
Maintenance accounts for approx. 30% of the life-cycle costs of a high-speed train, making it the largest rolling stock operating cost factor besides energy. Advancements in the Big data technologies and predictive analytics with M2M telematics are enabling deep insights into the machine operations by providing full functionality status in real time - giving rise to optimal maintenance schedules,
improved machine availability and asset usage. Predictive maintenance, also known as Condition Based Maintenance (CBM) aims to reduce these unnecessary costs by basing the maintenance need on the actual condition of the machine rather than on preset schedules or assumptions.
A survey by Schneider Electric in the US revealed that predictive maintenance services can lead to 25% reduction in cost. Learn about industrial IoT framework that enables PdM
In conclusion, nanoprecise technology is transforming traditional maintenance practices by enabling condition-based maintenance. The ability to monitor industrial equipment in real-time ensures that potential faults can be detected and addressed before they develop into major problems. This approach not only improves efficiency but also saves companies significant amounts of time and money. With the ever-increasing complexity of industrial machinery, it is clear that condition monitoring through nanoprecise technology will continue to gain popularity as a critical tool for predictive maintenance. We encourage all companies to embrace this technology and enjoy its benefits in improving their maintenance operations.
Nanoprecise Data Services Pvt. Ltd.IndiQube- Edge Service CentreKhatha No. 571/630/6/4,(Sy No.6/4), Ambalipura Village,Outer Ring Road, Varthur Hobli,Bangalore-560103
Condition Based Monitoring and Maintenance - NanopreciseNanoprecise
Condition-based monitoring and maintenance (CBM) is a proactive approach to equipment management that has gained popularity in recent years. It involves using sensors, data analytics, and machine learning algorithms to detect potential faults in machines before they cause a breakdown or failure. The primary goal of CBM is to maximize the uptime and performance of equipment while minimizing the cost of maintenance. By monitoring the Condition Monitoring Maintenance of machines in real-time, maintenance teams can schedule repairs when they are needed rather than at predetermined intervals, which can reduce downtime and save money on unnecessary repairs. we will explore the key concepts and benefits of CBM and examine how it differs from traditional methods of maintenance.
Guidelines to Understanding to estimate MTBFijsrd.com
To quantifying a reparable system or reliability we can use MTBF. It has been used for various decisions. MTBF is determining the reliability. For developing the MTBF model we can use Poisson distribution, Weibull model and Bayesian are the most popular approach. In this paper we are talking about complexities and misconceptions of MTBF and clarify in sequence what are the items and concerns that need to be consider in estimating MTBF.
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...Cognizant
Predictive analytics is a process of using statistical and data mining techniques to analyze historic and current data sets, create rules and predict future events. This paper outlines a game plan for effective implementation of predictive analytics.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Condition Monitoring of DC Motor using Artificial Intelligence Techniqueijsrd.com
The complexity of most steel industry always tends to create a problem in monitoring and supervision system. Prompt fault detection and diagnosis is a best way to handle and tackle this problem. Dc motor plays a very vital role in steel industry and there is a strong demand for their reliable and safe operation. The history of fault diagnosis and protection of electrical machines is as old as such machines themselves. However, nowadays, condition monitoring of electrical machines has become increasingly essential. It plays a very important role in their safe operation and helps to avoid heavy production losses in industry. The conditioning monitoring and fault-detection techniques of electrical machines have moved in recent years into artificial intelligence techniques. When an artificial intelligence technique is used, fault detection and evaluation can be accomplished without an expert. In this paper, artificial intelligence (AI) techniques are used to build a condition monitoring system that has incremental learning capabilities. The condition-monitoring of dc motor using AI technique schemes have concentrated on sensing specific failure modes in field windings.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
CONDITION-BASED MAINTENANCE USING SENSOR ARRAYS AND TELEMATICS
1. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
DOI : 10.5121/ijmnct.2013.3303 19
CONDITION-BASED MAINTENANCE USING SENSOR
ARRAYS AND TELEMATICS
Gopalakrishna Palem
Symphony-Teleca Corporation, Bangalore, India
Gopalakrishna.Palem@Yahoo.com
ABSTRACT
Emergence of uniquely addressable embeddable devices has raised the bar on Telematics capabilities.
Though the technology itself is not new, its application has been quite limited until now. Sensor based
telematics technologies generate volumes of data that are orders of magnitude larger than what operators
have dealt with previously. Real-time big data computation capabilities have opened the flood gates for
creating new predictive analytics capabilities into an otherwise simple data log systems, enabling real-time
control and monitoring to take preventive action in case of any anomalies. Condition-based-maintenance,
usage-based-insurance, smart metering and demand-based load generation etc. are some of the predictive
analytics use cases for Telematics. This paper presents the approach of condition-based maintenance using
real-time sensor monitoring, Telematics and predictive data analytics.
KEYWORDS
Telematics, Preventive Maintenance, Predictive Maintenance, Sensor arrays
1. INTRODUCTION
Maintenance, considered often as a non-value add function, is always under a constant pressure
from the top management to contribute more for costs reduction, keep the machines in excellent
working condition, all the while satisfying the stringent safety and operational requirements.
Towards this end manufacturers and operators usually employ various maintenance strategies, all
of which and can be broadly categorized as below:
• Corrective Maintenance
• Preventive Maintenance
• Predictive Maintenance
Corrective maintenance is the classic Run-to-Failure reactive maintenance that has no special
maintenance plan in place. The machine is assumed to be fit unless proven otherwise.
• Cons:
• High risk of collateral damage and secondary failure
• High production downtime
• Overtime labour and high cost of spare parts
• Pros:
• Machines are not over-maintained
• No overhead of condition monitoring or planning costs
Preventive maintenance (PM) is the popular periodic maintenance strategy that is actively
employed by all manufacturers and operators in the industry today. An optimal breakdown
2. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
20
window is pre-calculated (at the time of component design or installation, based on a wide range
of models describing the degradation process of equipment, cost structure and admissible
maintenance etc.), and a preventive maintenance schedule is laid out. Maintenance is carried-out
on those periodic intervals, assuming that the machine is going to break otherwise.
• Cons:
• Calendar-based maintenance: Machines are repaired when there are no faults
• There will still be unscheduled breakdowns
• Pros:
• Fewer catastrophic failures and lesser collateral damage
• Greater control over spare-parts and inventory
• Maintenance is performed in controlled manner, with a rough estimate of costs well-
known ahead of time
Predictive Maintenance, also known as Condition-based maintenance (CBM) is an emerging
alternative to the above two that employs predictive analytics over real-time data collected
(streamed) from parts of the machine to a centralized processor that detects variations in the
functional parameters and detects anomalies that can potentially lead to breakdowns. The real-
time nature of the analytics helps identify the functional breakdowns long before they happen but
soon after their potential cause arises.
• Pros:
• Unexpected breakdown is reduced or even completely eliminated
• Parts are ordered when needed and maintenance performed when convenient
• Equipment life is maximized
• Cons:
• High investment costs
• Additional skills might be required
Condition-based maintenance differs from schedule-based maintenance by basing maintenance
need on the actual condition of the machine rather than on some pre-set schedule.
For example, a typical schedule-based maintenance strategy demands automobile operators to
change the engine oil, say after every 3,000 to 5,000 Miles travelled. No concern is given to the
actual condition of vehicle or performance capability of the oil.
If on the other hand, the operator has some way of knowing or somehow measuring the actual
condition of the vehicle and the oil lubrication properties, he/she gains the potential to extend the
vehicle usage and postpone oil change until the vehicle has travelled 10,000 Miles, or perhaps
pre-pone the oil change in case of any abnormality.
In the following sections we look into what constitutes a condition-based maintenance solution,
the steps involved in implementing one and an overall solution methodology.
3. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
21
2. CONDITION-BASED MAINTENANCE
Predictive analytics in combination with sensor based telematics provides deep insights into the
machine operations and full functionality status – giving rise to optimal maintenance schedules
with improved machine availability.
Underlying schedule-based maintenance is the popular belief that machine failures are directly
related to machine operating age, which studies indicate not to be true always. Failures are not
always linear in nature. Studies indicate that 89% of the problems are random with no direct
relation to the age [2]. Table 1 showcases some of these well-known failure patterns and their
conditional probability (Y-axis) with respect to Time (X-axis).
Table 1. Failure Conditional Probability Curves
Age-Related = 11% Random = 89%
Wear out
Type A = 2%
Infant Mortality
Type B = 68%
Bathtub
Type E = 4%
Initial Break-in Period
Type C = 7%
Fatigue
Type F = 5%
Relatively Constant
Type D = 14%
Complex items frequently demonstrate some infant mortality, after which their failure probability
either increases gradually or remains constant, and a marked wear-out age is not common.
Considering this fact, the chance of a schedule-based maintenance avoiding a potential failure is
low, as there is every possibility that the system can fail right after a scheduled maintenance.
Thus, preventive maintenance imposes additional costs of repair. Condition-based maintenance
reduces such additional costs by scheduling maintenance if and only when a potential breakdown
symptom is identified.
Preventive Maintenance
Predictive Maintenance
Figure 1. Condition-based maintenance schedules are flexible and cost-saving
4. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
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However, the costs of monitoring equipment and monitoring operations should not exceed the
original asset replacement costs; lest the whole point of condition-based maintenance becomes
moot. Internal studies conducted with our customers have estimated that a properly functioning
CBM program can provide savings of 8% to 12% over the traditional maintenance schemes. They
indicated the following industrial average savings resultant from initiation of a functional
predictive maintenance program:
• Reduction in maintenance costs: 25% to 30%
• Elimination of breakdowns: 70% to 75%
• Reduction in equipment or process downtime: 35% to 45%
• Increase in production: 20% to 25%
Apart from the above, improved worker and environment safety, increased component
availability, better product quality etc. are making more and more manufacturers and operators
embrace CBM based management solutions.
2.1. SOLUTION ENABLERS
A Condition-based maintenance management (CBMM) solution is enabled by three major
technology enhancements over a traditional maintenance solution:
1. Remote Sensor Monitoring & Data Capturing
2. Real-time Stream Processing of Sensor Data
3. Predictive Analytics
CBMM solutions essentially operate by having sensors attached to remote assets (mobile or
stationary) that send continuous streams of data about the assets’ operational conditions to a
monitoring station that then analyses them in real-time using predictive analytic models and
detects any problems in the behaviour or state of the asset. Once a problem is detected,
appropriate pre-configured action is taken to notify the operator or manufacture for corrective
action. The monitoring station in question can be on the same network as that of the sensors or it
could be in a remote location far away from them, connected through wide area networks or
satellite networks.
Figure 2. Condition-based maintenance using sensor arrays and telematics
5. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
23
Nature of the sensors being monitored, frequency of the data getting collected and precision of the
analytic models being used – all affect the quality of the prediction results. Thus, it is imperative
that manufacturers and operators define all these parameters with utmost care while deploying a
CBMM system. This, however, entails a thorough understanding of the system under operation
and expects a clear-cut answer as to what is being monitored and what is expected out of such
monitoring. Some of the questions that can help manufacturers and operators along those lines
are:
For monitoring:
• Which parts of the system or asset are expected to be monitored?
• What type of data is expected to be collected and which type of sensors give such
data? For example, visual data, thermal data etc.
• What is the expected frequency for the data collection?
• How should any failures in the sensors be handled?
For real-time stream collection and processing at the monitoring station:
• What is the acceptable data processing latency?
• How to deal with imperfections in the received data? For example, a faulty sensor
sending incorrect data
• What should be done with the collected data after processing?
For Analytics sub-system:
• Which analysis technique accurately models the asset/system behaviour?
• What is the definition of acceptable behaviour and anomaly?
• What should be the response in case of any anomaly detection?
• What should be the reasonable timeframe between anomaly detection and corrective
action?
• How to deal with situations where there are multiple anomalies detected at the same
time?
Generic and security related questions:
• Who should be allowed to access the collected data and analysis results?
• What is the change management process required in case one wants to tune the
tracking and analysis parameters?
The following section briefly summarizes some of the standard methods we use in the CBMM
systems we deploy for our customers and can help in answering above questions.
2.2. THE METHODOLOGY
The primary component in a condition-based maintenance management solution is a sensor array
and the measurements it provide. Some of the widely used measurement techniques in the
industry are:
• Temperature Measurement: Thermal indicators, such as temperature-sensitive paint,
thermography etc., help detect potential failures arising out of temperature changes in the
equipment. Excessive mechanical friction, degraded heat transfer, poor electrical
connections are some of the problems that can be detected with this type of measurement.
Method Description Applications
Point
Temperature
A thermocouple or RTD
Can be used on all accessible
surfaces
Area Pyrometer
IR radiation measured from a
surface, often with laser sight
Good for walk around temperature
checks on machines and panels
6. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
24
Temperature
Paint
Chemical indicators calibrated
to change colors at specified
temperature
Works great for inspection rounds
Thermography
Handheld still or video camera
sensitive to emitted IR
Best for remote monitoring.
Requires good training
• Dynamic Monitoring: Spectrum analysis, shock pulse analysis are some of the dynamic
monitoring methods that measure and analyse energy emitted from mechanical equipment
in the form of waves, vibration, pulses and acoustic effects. Wear and tear, imbalance,
misalignment and internal surface damage are some of the problems that can be detected
with this type of measurement.
Method Description Applications
ISO Filtered
velocity
2Hz-1kHz filtered velocity A general condition indicator
SPM
Carpet and Peak related to
demodulation of sensor
resonance around 30kHz
Single value bearing indicator
method
Acoustic Emission
Distress & dB, demodulates
a 100kHz carrier sensitive to
stress waves
Better indicator than ISO
velocity, without the ISO
comfort zone
Vibration Meters
Combine velocity, bearing
and acceleration techniques
ISO Velocity, envelope and
high frequency acceleration
give best performance
4-20mA sensors
Filter data converted to
DCS/PLC compatible signal
Useful to home-in on specific
problems by special order
• Fluid Analysis: Ferrography, particle counter testing are some of the fluid analysis
methods performed on different types of oils, such as lubrication, hydraulic, insulation oil
etc., to identify any potential problems of wear and tear in the machines. Machine
degradation, oil contamination, improper oil consistency, oil deterioration are some of the
problems that can be detected with this method. The main areas of analysis in this are:
• Fluid physical properties: Viscosity, appearance
• Fluid chemical properties: TBN, TAN, additives, contamination, % water
• Fluid contamination: ISO cleanliness, Ferrography, Spectroscopy, dissolved
gases
• Machine health: Wear metals associated with plant components
• Corrosion Monitoring: Methods such as Coupon testing, corrometer testing help identify
the extent of corrosion, corrosion rate and state (active/passive corrosion) for the
materials used in the asset.
• Non-destructive Testing: Involves using non-destructive methods, such as X-Rays,
ultrasonic etc., to detect any potential anomalies arising internal to the asset structure.
Most of these tests can be performed while the asset is online and being used.
• Electrical testing and Monitoring: High potential testing, power signal analysis are some
of the prominent electrical condition monitoring mechanisms that try to identify any
changes in the system properties, such as resistance, conductivity, dielectric strength and
potential. Electrical insulation deterioration, broken motor rotor bars and shorted motor
7. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
25
stator lamination etc. are some of the problems that can be detected with this type of
mechanism.
• Observation and Surveillance: Visual, audio and touch inspection criteria are some of the
surveillance condition monitoring techniques based on the human sensory capabilities.
They act as supplement to other condition-monitoring techniques and help detect
problems such as loose/worn parts, leaking equipment, poor electrical and pipe
connections, stream leaks, pressure relief valve leaks and surface roughness changes etc.
Once the appropriate measurement mechanisms are in place, the next step is the event definition
phase: to define what constitutes acceptable system behaviour and what is to be considered as
anomaly. It is useless to put costly monitoring equipment in place, without knowing what to
expect out of it. Expert opinion and judgment (such as manufacturer’s recommendations),
published information (such as case studies), historical data etc. are some of the good sources that
can help in this task. The definition of anomaly should be unambiguous and easy to detect. If the
cost of anomaly detection far exceeds the costs of consequences of that anomaly, then it is not a
valid scenario for implementing CBMM system.
The next step that follows the event definition phase is, determining event inspection frequency.
Frequency of any of form of condition-based-maintenance is based on the fact that most failures
do not occur instantaneously, and that it is often possible to detect them during their final stages
of deterioration. If evidence can be found that something is in the final stages of failure, it is
possible to take action for preventing it from failing completely and/or avoid the consequences.
Figure 1. Measurement Frequencies can be determined with P-F Interval
The failure behaviour typically exhibited by majority of the systems in operation is as showcased
in Figure 3. During operation, over a period of time, the systems enter a phase of potential failure
(P), and start displaying few early signs of wear & tear and other stressful behaviours that if
neglected finally lead to full functional failure (F). For most of the systems the time interval
between the potential failure point (P) and full function failure (F) is large enough to allow
detection and prevention of the failure.
This time gap between P and F is what is popularly known as the P-F Interval in the literature,
and any cost-effective maintenance strategy should try to maximize on it. The P-F Interval could
be in hours or days, or even weeks or months, based on the complexity of the system and the unit
of measurement - for it is not uncommon to see the P-F Interval being measured in non-time
8. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
26
units, such as stop-start cycles or units of output etc. Based on the failure mode and the unit of
measurement, the P-F Interval can end up varying from fractions of a second to several decades
(on temporal scale).
Be whatever the unit of measurement and the P-F interval, a successful CBMM system should be
capable of detecting the early signals after P and respond to them long before F. The response
action typically consists of multiple steps (as laid out below) and should all be accompanied
within the P-F interval.
1. Analysing the root-cause based on detected early signals
2. Planning corrective action based on the analysed root-cause
3. Organizing the resources to implement the laid out plan
4. Actual implementation of the corrective action plan
The amount of time needed for these response actions usually vary, from a matter of hours (e.g.
until the end of operating cycle or end of shift), minutes (e.g. to clear people from a failing
building), to weeks or even months (e.g. until a major shutdown).
Thus, it is a common practice to use the inspection interval to be half the P-F interval. This will
ensure that there is at-least half the P-F interval remaining after the potential-failure detection for
corrective action plan. However, it should be noted that most of the times earlier the corrective
action plans are implemented, lower the cost – in which cases, some other smaller fraction of P-F
interval can be used as the inspection interval, so that potential problems can be detected as early
as possible and rectified.
However, an important point to be remembered is P-F interval is not an easy metric to be
computed. It varies from asset type to asset type, environment to environment and even from one
asset to another with in the same asset type (based on its previous fault history and working
conditions). Understanding the failure patterns, and identifying the class of pattern to which the
asset belongs, its past fault history, manufacturer’s recommendations, operating conditions, expert
judgment etc. are some of the sources that can help in arriving at an accurate P-F interval for any
given asset/system.
At each inspection interval, the CBMM system collects data from sensors and uses one of the
following methods to determine the condition of the asset being monitored:
• Trend Analysis: Reviews the data to find if the asset being monitored is on an obvious
and immediate downward slide toward failure. Typically a minimum of three monitoring
points are recommended for arriving at a trend accurately as a reliable measure to find if
the condition is deprecating linearly.
• Pattern recognition: Decodes the causal relations between certain type of events and
machine failures. For example, after being used for a certain product run, one of the
components used in the asset fails due to stresses that are unique to that run
• Critical range and limits: Tests to verify if the data is within a critical range limit (set by
professional intuition)
• Statistical process analysis: Existing failure record data (retrieved from warranty claims,
data archives and case-study histories) is driven through analytical procedures to find an
accurate model for the failure curves and the new data is compared against those models
to identify any potential failures.
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27
Based on the failure mode and asset class the right method for the prediction can vary. For
example, assets that fall into type E class (bathtub pattern) usually benefit from Weibull
distribution, while split system approach is used for complex systems with multiple sub-systems.
Stream processing the arriving data can help build the trend analysis and critical range limits, but
to accurately process pattern recognition and statistical model building methods, past history data
is as important as the new arriving data. Thus, typically CBM management systems should keep
record of old data for some reasonable amount of time before they are archived or destroyed. This
time period varies from domain to domain and may even be regulated by local country laws. For
example, financial fraud records may need to be kept active for longer time, in the range of 7 to
15 years per se, while flight records generated from airplane internal sensors are typically
discarded after the journey completion (primarily due to it being voluminous, though this trend
could soon change as the big data warehousing gets more prominent).
Another reason the old data streams become important is to identify any potential outliers in the
streamed-in data from the sensors. While monitoring for the faults in the assets, it is possible that
the sensor that is taking the readings, being a machine itself, could fail and start sending faulty
records. Intelligent CBM management systems capable of detecting such outliers will try to
isolate these faulty sensors and notify the appropriate personal for corrective action, or substitute
it with proper estimated data based on previous records. In either case, human inspection is as
much necessary as a completely automated monitoring system – for automation only
complements the human surveillance efforts, not replace them. Thus, many automated monitoring
systems provide a way for manual override for configurable parts of their functionality.
Figure 2. Condition-based maintenance management system architecture
Typically a CBM management solution will have an administrative console that lets the operators
define and update various parameters, such as critical limits, response notifications, default
corrective actions etc. Advanced management systems also allow the administrators to access the
monitoring solution functionality remotely through web UI and mobile UI, capable of sending
periodic digests weekly or monthly for pre-configured stake-holders reporting the status of the
asset being monitored on a regular basis. With architectures like the one shown in Figure 4 and
common interface standards such as IEEE 1451, IEEE 1232, MIMOSA and OSA-CBM,
10. International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 3, No.3, June 2013
28
advanced management systems integrations become possible among disparate software and
hardware components from different vendors, all working hard together just for one single
purpose – to provide the operators maximum usage out of their assets.
3. CONCLUSIONS
Condition-based maintenance is based on the principle of using real-time data to prioritize and
optimize maintenance resources. Such a system will determine the equipment's health, and act
only when maintenance is actually necessary. Telematics development (such as IPV6, 3G and 4G
LTE) in the recent times in combination with big-data real-time stream analytics is opening new
opportunities for manufacturers and asset owners to save costs and optimize resource usage in
innovative ways. Condition-based maintenance management systems built around real-time
sensor monitoring and telematics technologies offer flexibility and cost-savings in terms of
providing greater control over when to perform the maintenance, which parts to pre-order and
how the optimally schedule the labour.
REFERENCES
[1] Weibull, W, (1951) "A statistical distribution function of wide applicability", Journal of Applied
Mechanics-Trans. ASME, Vol. 18, No. 3: pp. 293–297.
[2] Nowlan, F. Stanley, and Howard F. Heap, (1978) Reliability-Centred Maintenance. Department of
Defense, Report Number AD-A066579 Washington, D.C. 1978.
[3] Pérez, Angel Torres; Hadfield, Mark. (2011). "Low-Cost Oil Quality Sensor Based on Changes in
Complex Permittivity." Sensors 11, no. 11: 10675-10690.
[4] J. CUENA, M. MOLINA, (2000) “The role of knowledge modelling techniques in software
development: a general approach based on a knowledge management tool”, International Journal
of Human-Computer Studies, Vol. 52, No. 3, pp. 385-421
[5] G. Abdul-Nour, H. Beaudoin, P. Ouellet, R. Rochette, S. Lambert, (2000) “A reliability based
maintenance policy; a case study”, Computers & Industrial Engineering, Vol. 35, No. 3-4, pp. 591-
594
[6] Hansen, T., Dirckinck-Holmfeld, L., Lewis, R., & Rugelj, J. (1999). Using telematics to support
collaborative knowledge construction. Collaborative learning: Cognitive and computational
approaches, 169-196.
Authors
Gopalakrishna Palem is a Corporate Technology Strategist specialized in Distributed Computing
technologies and Cloud operations. During his 12+ year tenure at Microsoft and Oracle, he helped many
customers build their high volume transactional systems, distributed render pipelines, advanced
visualization & modelling tools, real-time dataflow dependency-graph architectures, and Single-sign-on
implementations for M2M telematics. When he is not busy working, he is actively engaged in driving
open-source efforts and guiding researchers on Algorithmic Information Theory, Systems Control and
Automata, Poincare recurrences for finite-state machines, Knowledge modelling in data-dependent
systems and Natural Language Processing (NLP).