The document discusses condition monitoring of machinery using artificial intelligence techniques. It presents:
1) Condition monitoring and artificial intelligence can help automate monitoring of steady and unsteady equipment by analyzing variable parameters like loads, speeds and temperatures.
2) The theory of condition monitoring and artificial intelligence is explained, along with experimental work on methodology and results.
3) Monitoring multi-modal machinery requires techniques spanning sensing, segmentation, feature extraction, classification and post-processing to determine machinery health from noisy parameter data.
The system uses a J2ME mobile phone with a camera to capture video. It applies background subtraction and motion detection algorithms to segment moving objects from the background. When motion is detected above a threshold, the system sends an alert message (SMS or MMS) to predefined phone numbers to notify users. The system has advantages of low cost, little memory usage, ease of use, and mobility. It provides a simple solution for wireless security and monitoring.
This document discusses data analysis techniques for refraction tomography including data conversion, signal killing, picking approaches, and model geometry. It provides instructions on installing picking software, naming converted data files sequentially, fixing header sizes, deleting unwanted traces based on component, and approaches for manual and automated first break picking. Examples of clear seismic records that make first arrival picking easy are also shown.
This document provides technical specifications for the μQC Microscope. It has a vertical scan range of ≤ 500μm and vertical and lateral resolutions of ≤ 10nm and 0.13-2.21μm respectively. The microscope uses non-contact white light interferometry and has capabilities such as automatic depth, volume, and surface roughness measurements. It is suited for surface characterization of materials like ceramics and metals.
The testo 885 is a precise thermal imager with a 320 x 240 pixel detector, SuperResolution technology, and a temperature sensitivity of less than 30 mK. It features an ergonomic design, intuitive hybrid operation, and interchangeable lenses. The testo 885 measures temperatures from -20°C to 1200°C and includes site recognition, digital camera, and video measurement capabilities.
This document summarizes a master's thesis on developing a library for organizing image recognition systems. It outlines the thesis, which proposes a modular plug-in based system called AMORS that standardizes interfaces between acquisition, processing, and display modules. It then discusses applications of AMORS to automatic recognition of micro-objects, human brain cells (using segmentation, features and self-organizing maps), and counting bacteria. Finally, it describes the library developed containing classes to handle images, objects and configurations to support portability.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document discusses various techniques for edge detection in digital images, including differential operators, log operators, Canny operators, and binary morphology.
2) It first performs wavelet-based denoising on input images to remove noise before edge detection.
3) It then applies different edge detection operators and compares their advantages and disadvantages through simulations. Binary morphology is shown to obtain better edge features compared to other operators.
4) The overall goal is to extract clear and complete edge profiles from images to aid in tasks like image segmentation.
The testo 890 thermal imager provides the highest image quality thanks to its 640 x 480 pixel detector and Germanium optics. It can record extremely high resolution thermal images up to 1280 x 960 pixels using SuperResolution technology. The testo 890 has ideal ergonomics with a fold-out, rotatable display and ergonomic handle, as well as intuitive hybrid touchscreen and joystick operation. It offers full radiometric video measurement and analysis capabilities.
The system uses a J2ME mobile phone with a camera to capture video. It applies background subtraction and motion detection algorithms to segment moving objects from the background. When motion is detected above a threshold, the system sends an alert message (SMS or MMS) to predefined phone numbers to notify users. The system has advantages of low cost, little memory usage, ease of use, and mobility. It provides a simple solution for wireless security and monitoring.
This document discusses data analysis techniques for refraction tomography including data conversion, signal killing, picking approaches, and model geometry. It provides instructions on installing picking software, naming converted data files sequentially, fixing header sizes, deleting unwanted traces based on component, and approaches for manual and automated first break picking. Examples of clear seismic records that make first arrival picking easy are also shown.
This document provides technical specifications for the μQC Microscope. It has a vertical scan range of ≤ 500μm and vertical and lateral resolutions of ≤ 10nm and 0.13-2.21μm respectively. The microscope uses non-contact white light interferometry and has capabilities such as automatic depth, volume, and surface roughness measurements. It is suited for surface characterization of materials like ceramics and metals.
The testo 885 is a precise thermal imager with a 320 x 240 pixel detector, SuperResolution technology, and a temperature sensitivity of less than 30 mK. It features an ergonomic design, intuitive hybrid operation, and interchangeable lenses. The testo 885 measures temperatures from -20°C to 1200°C and includes site recognition, digital camera, and video measurement capabilities.
This document summarizes a master's thesis on developing a library for organizing image recognition systems. It outlines the thesis, which proposes a modular plug-in based system called AMORS that standardizes interfaces between acquisition, processing, and display modules. It then discusses applications of AMORS to automatic recognition of micro-objects, human brain cells (using segmentation, features and self-organizing maps), and counting bacteria. Finally, it describes the library developed containing classes to handle images, objects and configurations to support portability.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document discusses various techniques for edge detection in digital images, including differential operators, log operators, Canny operators, and binary morphology.
2) It first performs wavelet-based denoising on input images to remove noise before edge detection.
3) It then applies different edge detection operators and compares their advantages and disadvantages through simulations. Binary morphology is shown to obtain better edge features compared to other operators.
4) The overall goal is to extract clear and complete edge profiles from images to aid in tasks like image segmentation.
The testo 890 thermal imager provides the highest image quality thanks to its 640 x 480 pixel detector and Germanium optics. It can record extremely high resolution thermal images up to 1280 x 960 pixels using SuperResolution technology. The testo 890 has ideal ergonomics with a fold-out, rotatable display and ergonomic handle, as well as intuitive hybrid touchscreen and joystick operation. It offers full radiometric video measurement and analysis capabilities.
Condition Monitoring Of Unsteadily Operating EquipmentJordan McBain
The document discusses techniques for condition monitoring of unsteadily operating equipment. It proposes a statistical parameterization approach involving segmenting vibration data based on steady speeds/loads, extracting statistical parameters from segments, and using novelty detection with support vectors to classify patterns as normal or faulted while accounting for changing operating conditions. Experimental results on gearbox data demonstrated superior fault detection performance compared to alternative approaches.
ME4AWSN - a Modeling Environment for Architecting WSNsIvano Malavolta
Advanced Software Engineering course - Guest Lecture
ME4AWSN - a Modeling Environment for Architecting WSNs
Here you can find the research paper presenting the concepts described in this lecture: http://goo.gl/XBB4k
This presentation has been developed in the context of the Advanced Software Engineering course at the DISIM Department of the University of L’Aquila (Italy).
http://www.di.univaq.it/malavolta
Principal Component Analysis For Novelty DetectionJordan McBain
This document summarizes a journal article that proposes using principal component analysis (PCA) for novelty detection in condition monitoring applications. It describes how PCA can be used to reduce the dimensionality of feature spaces while retaining most of the variation in the data. The authors modify the standard PCA technique to maximize the difference between the spread of normal data and the spread of outlier data from the mean of the normal data. They validate the approach on artificial and machinery vibration data and show it can effectively distinguish outliers. Future work could involve extending the technique to non-linear data using kernel methods.
Combining out - of - band monitoring with AI and big data for datacenter aut...Ganesan Narayanasamy
Andrea Bartolini presented a method for combining out-of-band monitoring with artificial intelligence and big data analytics to enable datacenter automation. Their system, called D.A.V.I.D.E., uses fine-grained power and performance monitoring of nodes through an embedded system. Data is collected and analyzed using MQTT, Cassandra, and Apache Spark. An autoencoder was trained on historical monitoring data to learn normal behavior and is used to detect anomalies through reconstruction error at the edge in real-time. Future work includes extending this approach for security and expanding it to larger systems.
The document summarizes a research project on multi-resolution data fusion using agent-based sensors. The project aims to develop collaborative signal processing techniques that are energy-aware, fault-tolerant, and progressively improve accuracy. Key accomplishments include developing mobile agent-based collaborative signal processing, energy-aware task scheduling algorithms, analytical battery modeling, and sensor deployment algorithms. The project has resulted in several publications and integrated some techniques successfully, while other integration efforts faced challenges.
This presentation proposes a remote biometric authentication system using video-object steganography over wireless networks. It uses principal component analysis (PCA) and discrete wavelet transform (DWT) to encrypt biometric signals, hide them in video objects, and transmit them securely. The encrypted biometric signals are inserted into the most significant wavelet coefficients of video objects during transmission. At the receiver end, fingerprint matching is used to authenticate users by extracting and decrypting the biometric signals from the video objects. The system aims to provide secure remote authentication using natural biometrics while maintaining efficiency and resistance against data loss during wireless transmission.
This document discusses systems for monitoring and controlling renewable energy plants. It covers various architectures for wind turbine and solar plant monitoring including control systems, communications networks, and data standards. Concepts around the IEC 61400-25 standard are explained for modeling wind turbine data. Various signal processing techniques are presented for fault detection in generators and gearboxes including relevant fault frequencies. Condition monitoring technologies are outlined along with classification algorithms like SVM. Finally, an integrated smart operations management platform called SmartOpex is introduced.
lesson 2 digital data acquisition and data processingMathew John
Digital data acquisition and processing are important for nondestructive evaluation (NDE). Data acquisition is needed to obtain quantitative information from test specimens in complex field environments. Data analysis techniques like noise reduction, feature extraction, and multi-parameter discrimination can then be used to interpret the data. Proper data acquisition, digital signal processing algorithms, and discrimination methods now allow NDE procedures to be automated and problems previously considered unsolvable to be addressed.
QUALITY ASSESSMENT FOR LIDAR POINT CLOUD REGISTRATION USING IN-SITU CONJUGATE...grssieee
This document summarizes a study that assessed the quality of LIDAR point cloud registration using in-situ conjugate features. In-situ features from two LIDAR scans of a bridge, including pillars, rails, and surfaces, were used without additional targets. A weighted NISLT approach estimated transformation parameters from feature correspondences. Registration quality was evaluated using absolute consistency, measuring positional alignment, and relative similarity, measuring geometric similarity. The registration of the bridge scans achieved sub-centimeter consistency and similarity, within the resolution of the original LIDAR data. The proposed approach reduces costs by using inherent features instead of targets and provides a complete quality assessment.
This document discusses an ultrasonic tactile display and a field characterization robot. The display uses ultrasound interference to create a focal point for tactile stimulation. The ultrasound is modulated with low frequencies for detection by mechanoreceptors. Different modulation schemes produce varied tactile sensations. The robot uses stepper motors and sensors to precisely map ultrasound fields in 3D space over user-defined volumes and resolutions, and analyzes the data.
Neuromorphic Engineering is the new branch developing too much.Temporal Contrast Vision Sensor is one of the methods for Contour detection for a moving object.
Deep Learning Based Voice Activity Detection and Speech EnhancementNAVER Engineering
The document summarizes speech recognition front-end technologies including voice activity detection (VAD) and speech enhancement. It discusses conventional signal processing based approaches and more recent deep learning based methods. For VAD, it describes adaptive context attention models that can dynamically adjust the context used based on noise type and SNR. For speech enhancement, it proposes a two-step neural network approach consisting of a prior network that makes multiple predictions from noisy features and a post network that combines these using a boosting method to produce enhanced features, allowing end-to-end training without an explicit masking step. The approach aims to better exploit neural network modeling power while reducing computation cost compared to conventional methods or single-step deep learning frameworks.
This document provides an overview of artificial intelligence and machine learning techniques, including:
1. It defines artificial intelligence and lists some common applications such as gaming, natural language processing, and robotics.
2. It describes different machine learning algorithms like supervised learning, unsupervised learning, reinforced learning, and their applications in areas such as healthcare, finance, and retail.
3. It explains deep learning concepts such as neural networks, activation functions, loss functions, and architectures like convolutional neural networks and recurrent neural networks.
Positioning techniques in 3 g networks (1)kike2005
Independent Study Presentation on Positioning Techniques in 3G Networks. The presentation discusses [1] positioning parameters in 3G networks such as RSCP, RSS, RTT, and AoA; and [2] positioning techniques including enhancements to the basic Cell ID method, OTDOA methods using IPDL and CVB, the Database Correlation Method using power delay profiles, and the Pilot Correlation Method using pilot signal measurements. Simulation results are presented showing the accuracy of some of these techniques.
Ifu accelerated life test april 2010 - ian soukupcahouser
The document describes an accelerated life test for the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) fiber positioning system. A hexapod test apparatus was designed to replicate the dynamic motion of the fiber segments over a 5-year projected lifespan in a compressed 2-3 month period. Key design parameters included the hexapod travel limits, actuator speed and force capabilities, and custom end joints to handle misalignments. The test aims to continuously measure changes in focal ratio for individual fibers to evaluate the optical performance over repeated flexing cycles.
IMU (inertial measurement unit) has already played significant roles in the control system of aerospace and other vehicle platforms. Due to the maturity and low cost of MEMS technology, IMU starts to penetrate consumer products such as smartphone, wearables and VR/AR devices.
This sharing will focus on the general introduction of IMU components, signal characteristics and application concepts, with an attempt to guide those who is interested in the IMU-based system integration and algorithm development.
Big data analytics Big data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analytics
Condition Monitoring Of Unsteadily Operating EquipmentJordan McBain
The document discusses techniques for condition monitoring of unsteadily operating equipment. It proposes a statistical parameterization approach involving segmenting vibration data based on steady speeds/loads, extracting statistical parameters from segments, and using novelty detection with support vectors to classify patterns as normal or faulted while accounting for changing operating conditions. Experimental results on gearbox data demonstrated superior fault detection performance compared to alternative approaches.
ME4AWSN - a Modeling Environment for Architecting WSNsIvano Malavolta
Advanced Software Engineering course - Guest Lecture
ME4AWSN - a Modeling Environment for Architecting WSNs
Here you can find the research paper presenting the concepts described in this lecture: http://goo.gl/XBB4k
This presentation has been developed in the context of the Advanced Software Engineering course at the DISIM Department of the University of L’Aquila (Italy).
http://www.di.univaq.it/malavolta
Principal Component Analysis For Novelty DetectionJordan McBain
This document summarizes a journal article that proposes using principal component analysis (PCA) for novelty detection in condition monitoring applications. It describes how PCA can be used to reduce the dimensionality of feature spaces while retaining most of the variation in the data. The authors modify the standard PCA technique to maximize the difference between the spread of normal data and the spread of outlier data from the mean of the normal data. They validate the approach on artificial and machinery vibration data and show it can effectively distinguish outliers. Future work could involve extending the technique to non-linear data using kernel methods.
Combining out - of - band monitoring with AI and big data for datacenter aut...Ganesan Narayanasamy
Andrea Bartolini presented a method for combining out-of-band monitoring with artificial intelligence and big data analytics to enable datacenter automation. Their system, called D.A.V.I.D.E., uses fine-grained power and performance monitoring of nodes through an embedded system. Data is collected and analyzed using MQTT, Cassandra, and Apache Spark. An autoencoder was trained on historical monitoring data to learn normal behavior and is used to detect anomalies through reconstruction error at the edge in real-time. Future work includes extending this approach for security and expanding it to larger systems.
The document summarizes a research project on multi-resolution data fusion using agent-based sensors. The project aims to develop collaborative signal processing techniques that are energy-aware, fault-tolerant, and progressively improve accuracy. Key accomplishments include developing mobile agent-based collaborative signal processing, energy-aware task scheduling algorithms, analytical battery modeling, and sensor deployment algorithms. The project has resulted in several publications and integrated some techniques successfully, while other integration efforts faced challenges.
This presentation proposes a remote biometric authentication system using video-object steganography over wireless networks. It uses principal component analysis (PCA) and discrete wavelet transform (DWT) to encrypt biometric signals, hide them in video objects, and transmit them securely. The encrypted biometric signals are inserted into the most significant wavelet coefficients of video objects during transmission. At the receiver end, fingerprint matching is used to authenticate users by extracting and decrypting the biometric signals from the video objects. The system aims to provide secure remote authentication using natural biometrics while maintaining efficiency and resistance against data loss during wireless transmission.
This document discusses systems for monitoring and controlling renewable energy plants. It covers various architectures for wind turbine and solar plant monitoring including control systems, communications networks, and data standards. Concepts around the IEC 61400-25 standard are explained for modeling wind turbine data. Various signal processing techniques are presented for fault detection in generators and gearboxes including relevant fault frequencies. Condition monitoring technologies are outlined along with classification algorithms like SVM. Finally, an integrated smart operations management platform called SmartOpex is introduced.
lesson 2 digital data acquisition and data processingMathew John
Digital data acquisition and processing are important for nondestructive evaluation (NDE). Data acquisition is needed to obtain quantitative information from test specimens in complex field environments. Data analysis techniques like noise reduction, feature extraction, and multi-parameter discrimination can then be used to interpret the data. Proper data acquisition, digital signal processing algorithms, and discrimination methods now allow NDE procedures to be automated and problems previously considered unsolvable to be addressed.
QUALITY ASSESSMENT FOR LIDAR POINT CLOUD REGISTRATION USING IN-SITU CONJUGATE...grssieee
This document summarizes a study that assessed the quality of LIDAR point cloud registration using in-situ conjugate features. In-situ features from two LIDAR scans of a bridge, including pillars, rails, and surfaces, were used without additional targets. A weighted NISLT approach estimated transformation parameters from feature correspondences. Registration quality was evaluated using absolute consistency, measuring positional alignment, and relative similarity, measuring geometric similarity. The registration of the bridge scans achieved sub-centimeter consistency and similarity, within the resolution of the original LIDAR data. The proposed approach reduces costs by using inherent features instead of targets and provides a complete quality assessment.
This document discusses an ultrasonic tactile display and a field characterization robot. The display uses ultrasound interference to create a focal point for tactile stimulation. The ultrasound is modulated with low frequencies for detection by mechanoreceptors. Different modulation schemes produce varied tactile sensations. The robot uses stepper motors and sensors to precisely map ultrasound fields in 3D space over user-defined volumes and resolutions, and analyzes the data.
Neuromorphic Engineering is the new branch developing too much.Temporal Contrast Vision Sensor is one of the methods for Contour detection for a moving object.
Deep Learning Based Voice Activity Detection and Speech EnhancementNAVER Engineering
The document summarizes speech recognition front-end technologies including voice activity detection (VAD) and speech enhancement. It discusses conventional signal processing based approaches and more recent deep learning based methods. For VAD, it describes adaptive context attention models that can dynamically adjust the context used based on noise type and SNR. For speech enhancement, it proposes a two-step neural network approach consisting of a prior network that makes multiple predictions from noisy features and a post network that combines these using a boosting method to produce enhanced features, allowing end-to-end training without an explicit masking step. The approach aims to better exploit neural network modeling power while reducing computation cost compared to conventional methods or single-step deep learning frameworks.
This document provides an overview of artificial intelligence and machine learning techniques, including:
1. It defines artificial intelligence and lists some common applications such as gaming, natural language processing, and robotics.
2. It describes different machine learning algorithms like supervised learning, unsupervised learning, reinforced learning, and their applications in areas such as healthcare, finance, and retail.
3. It explains deep learning concepts such as neural networks, activation functions, loss functions, and architectures like convolutional neural networks and recurrent neural networks.
Positioning techniques in 3 g networks (1)kike2005
Independent Study Presentation on Positioning Techniques in 3G Networks. The presentation discusses [1] positioning parameters in 3G networks such as RSCP, RSS, RTT, and AoA; and [2] positioning techniques including enhancements to the basic Cell ID method, OTDOA methods using IPDL and CVB, the Database Correlation Method using power delay profiles, and the Pilot Correlation Method using pilot signal measurements. Simulation results are presented showing the accuracy of some of these techniques.
Ifu accelerated life test april 2010 - ian soukupcahouser
The document describes an accelerated life test for the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) fiber positioning system. A hexapod test apparatus was designed to replicate the dynamic motion of the fiber segments over a 5-year projected lifespan in a compressed 2-3 month period. Key design parameters included the hexapod travel limits, actuator speed and force capabilities, and custom end joints to handle misalignments. The test aims to continuously measure changes in focal ratio for individual fibers to evaluate the optical performance over repeated flexing cycles.
IMU (inertial measurement unit) has already played significant roles in the control system of aerospace and other vehicle platforms. Due to the maturity and low cost of MEMS technology, IMU starts to penetrate consumer products such as smartphone, wearables and VR/AR devices.
This sharing will focus on the general introduction of IMU components, signal characteristics and application concepts, with an attempt to guide those who is interested in the IMU-based system integration and algorithm development.
Big data analytics Big data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analyticsBig data analytics
1. As submitted to Mechanical Systems and
Signal Processing
Jordan McBain, P.Eng.
2. Maintenance has advanced
considerably from reactive policies
Modern sensors, computers and algorithms have
set the stage
Health Monitoring of steady machinery widely
available
Few techniques are available for monitoring
unsteadily operating equipment
Techniques required for advanced equipment
such as electromechanical shovel,
variable duty hoists, etc.
◦ Subject to variable loads, speed,
◦ temperatures, etc.
3. Theory
◦ Condition Monitoring
◦ Artificial Intelligence (AI) Background
◦ AI for Monitoring Machinery
◦ Monitoring Multi-Modal Machinery
Experimental Work
◦ Methodology
◦ Results
◦ Future Work
4.
5. Machinery Maintenance Policy driven by:
◦ Availability of resources (spare parts, pers., capital)
◦ Importance of equipment
◦ Availability of technology and expertise
Modern Maintenance Policy evolved through:
◦ Run-to-Failure
◦ Periodic Maintenance
◦ Predictive Maintenance
Maintenance is delayed until some monitored
parameter of the equipment becomes erratic
Proactive
Balances resources
6. Benefits:
◦ Environment
◦ Safety
◦ Production
◦ Staff Shortages/Costs
◦ Scheduling
◦ Spare Parts (JIT)
◦ Insurance
◦ Life Extension
7.
8. Savvy technicians employ(ed) a screw driver
set atop a vibrating machine
◦ Resultant vibration of screw driver used by
technician to classify health
AUTOMATE THIS!
◦ More sensitive
◦ Earlier detection of faults
◦ Consistent, reliable measurements
Consistent, reliable classification
9. One branch of artificial-intelligence domain
Usually involves representing a state or object
to be indentified with a vector of
commensurate numerical values
Representative vector called a “pattern” or
“classification object”
Classification achieved by computing decision
surfaces around classes of objects
Example: biometric classification of
employees reporting to work
10. Feature Post-
Sensing Segmentation Classification
Extraction Processing
Measurements Selecting Reducing Plotting -Decision Support
(height, weight, measurement segmented values in -Also detect
eye colour) interval measurements n-dimensions enebriation
to key and fitting a -Pay
numbers boundary -Etc.
11. Feature Post-
Sensing Segmentation Classification
Extraction Processing
Employing sensors to collect relevant data
◦ Height, weight, eye colour, finger prints, image of
retina, DNA
Conditioning signals
◦ Filtering noise
12. Feature Post-
Sensing Segmentation Classification
Extraction Processing
Sensor data divided into useful chunks
◦ Separate employees from one another
Use a terminal for employees to sign in one at a time
Use image processing and separate employees from
each other in picture
One of the most difficult problems in pattern
recognition
13. Feature Post-
Sensing Segmentation Classification
Extraction Processing
Characterizes an object to be recognized by
measurements whose values are very similar for
objects in the same category
Invariant to irrelevant transformations
An ideal feature vector makes the job of
classification trivial (e.g. DNA)
The curse of dimensionality
◦ A balance between improvements from increased
dimensionality and increased need for data to describe
the space and added complexities
14. Feature Post-
Sensing Segmentation Classification
Extraction Processing
Employs full feature vector provided by the
feature extractor to assign the feature vector‟s
object to a category
Generalization – learning from a training set
extends well to unexperienced data
E.g. Neural Networks
◦ As one would fit a model to an experimental data set
with least-squares regression, in classification one
would fit a boundary around a class‟ data set
◦ Computationally equivalent tasks
But in classification, the problem is non-linear
15. Feature Post-
Sensing Segmentation Classification
Extraction Processing
Perform some action subsequent to
classification
Improve classification error based on context
◦ Employ multiple classifiers
16.
17. Goal:
◦ Divine state of machinery health from noisy
parameters
Techniques
◦ Ranging from thermography, eddy-current
measurement, oil analysis to vibration
18. • Accelerometers, acoustic emission, temperature
• Filter stationary machinery elements (fans, EMI, etc)
Sensing
• Use a standard length of vibration data (average other sensors according to the corresponding
time interval)
Segmentation • Use a variable length group of vibration data
• Auto-regressive models, MUSIC spectrum, statistics (mean, RMS, etc), order domain, etc.
Feature
Extraction
• Novelty detection (support vectors, neural network variants, etc)
Classification
• The foregoing is considered fault detection
• Consider: diagnostics, prognostics
Post-Processing • Potential responses: stop machinery, inform technician, update database, etc.
19. Heavily used in literature
Non-destructive, online, sensitive
Faults in rotating machinery have
strongly representative features
in the frequency domain
Consider bearings:
◦ Frequency Response a function of
Fault, Slippage, Noise
Diagrams from: Randall, B. State
of the Art in Machinery Monitoring, JSV
20. Motivation: addresses imbalance of data from
one class in relation to that of others
◦ Data from faulted states are difficult to collect
(economics, operation)
Sub problem of pattern recognition
◦ train on the “normal” class and then signal error when
behaviour deviates from itDecision boundary encircles
normal patterns
A wide variety of techniques available
Examine two:
◦ Boundaries containing a certain quantile of data (i.e. a
discordance test)
◦ Boundaries derived by Support Vectors
21. Support Vector Technique: Tax‟s Support
Vector Data Description (for Novelty
Detection)
◦ Attempts to fit a sphere of minimal radius around
normal data
◦ But a in a higher dimensional space (using the
“kernel trick”)
Generates a very flexible decision boundary in the
input space
22.
23.
24. Simplest machine
◦ damped spring system
mx cx kx f (t ) k c
n
m 2 km
◦ Frequency domain representation
1 1
H ( w) 2
m wn w2 j 2 wn w
◦ Forced with a function f (t ) A *sin( 0t )
25. With frequency-domain representation
A A
F( ) ( 0) ( 0 )
2 2
The system‟s output is given by X ( ) F ( )H ( )
1 1 A A
X( ) 2 2
( ( 0) ( 0 ))
m wn w0 j 2 wn w0 2 2
26. Underground mines
◦ Ventilation fans driven with VFD to optimize
efficiency
◦ Fans driven at one speed one day and then changed
to a different constant speed
New forcing function
A2 sin( 1t ), t 0
f (t )
A3 sin( 2t ), t 0
27. Examine function for one day (windowing)
t
f (t ) rect ( )* A4 sin( 3t )
Frequency representation (convolution
operator):
A A
F( ) Rect( ) ( ( 3) ( 3 ))
2 2
A A
sinc( ) ( ( 3 ) ( 3 ))
2 2
A
(sinc( 3 ) sinc( 3 ))
2
System‟s response to forcing, similar
◦ Spectral leakage and smearing by windowing
28. Consider function including instant of change
for a period of time 2*Tow
t 1 t 1
f (t ) rect (2 )* A2 sin( 1t ) rect (2 )* A3 sin( 3t )
2 2
Resultant frequency representation
A1 A1 A2 A2
F( ) Rect( ) ( ( 1) ( 1 )) Rect( ) ( ( 2) ( 2 ))
2 2 2 2 2 2 2 2
A A
(sinc( 1) sinc( 1 )) (sinc( 2 ) sinc( 2 ))
4 4
29. Sinc functions with sidelobes
◦ Introducing interference on spectrum
◦ Central frequencies contaminated with frequency
info from windowing function
◦ Info not solely indicative of health
30. Forced function with time varying frequency
f (t ) Ac cos(2 f ct cos(2 f mt ))
◦ f m as modulating frequency
◦ f c as carrier frequency
◦ modulation index
No closed form solution of fourier integral
Use bessel functions
31. (Mathematically) unlimited bandwidth
In practice 98% of bandwidth determined by
beta
32. Examining over a period of time (windowing)
◦ Introduces sinc functions mounted on impulses
◦ Consequence: spectral interference
Conclusion
◦ Frequency domain contains valuable info on:
System behaviour
Faults manifested in the form of changes in stiffness and
damping
Forcing function
◦ Info in frequency bands not limited to system
behaviour
33. Gear interaction modeled with:
mx cx kx f (t )
As suggested by
J- Kuang, A- Lin. Theoretical aspects of Torque responses in spur
gearing due to mesh stiffness variation, Mechanical Systems and
Signal Processing. 17 (2003) 255-271.
Assume
◦ Fixed load of L (Nm)
◦ Damping ratio of c=0.17
◦ Spring value k = k(t)
Normal assumptions of spring constant
◦ Clean frequency plot
◦ Obvious harmonics and sidebands
34. Spring stiffness varies with time
Consequence: non-linear frequency response
◦ Convolution introduced
2
m X ( ) cj X ( ) K ( ) X( ) F( )
35. Frequency response of k(t), modeled as simple
pulse train, is well known (RADAR, SONAR)
◦ Sync function as envelop to impulse train
Variable speed machinery
◦ Stiffness: variable pulse train
◦ I.e. Pulse Width Modulation
◦ No closed form Fourier integral
Bessel functions
◦ Transfer function not discernible
Numerical analysis necessary
Consequence
◦ Spectrum incredibly complex
◦ No simple band to monitor
36. Primary aggravators: load and speed
◦ Referred to as nuisance variables in the literature
In vibration monitoring
◦ Power of vibration a product of the effects of load and
speed
Relation between power and speed non-linear
Resonances!
Vibration a function of health and speed
Complex machinery an amalgamation of spring-like elements
Vibration in most mechanical systems involves periodic
oscillation of energy from potential to kinetic (according to
frequency response of spring approximation)
When machine is healthy, deviations in consequent vibrations
are small
37. When machine is healthy, deviations in consequent
vibrations are small
When health is poor, deviations due to speed become
significant
Stack: Damping in undamaged machinery is largely
insensitive to speed/load changes – damaged
machinery is not
38.
39.
40.
41. Feature Post-
Sensing Segmentation Classification
Extraction Processing
Segment vibration data into segments of
„steady‟ speed and load
◦ Segments defined by n-shaft rotations
Accounts for varying speed
Ensures coherent signal
Windowed (Gaussian Window – 70% overlap)
42. Steady speed/load not guaranteed
◦ But can generate segments with reasonable steadiness
and variance can be computed
Group vibration segments into bins of a selected
size
◦ Size effects how many classification objects in each bin
curse of dimensionality balanced against need for very fine
modal resolution
44. Signal processing technique
◦ Not a feature vector
◦ Not a fault detection technique
Resamples data at constant angular shaft intervals
◦ Rather than constant time intervals
Tachometers employed (2500 pulses per rev)
At max speed (500 rpm)
◦ 18 000 samples collected
◦ Tach pulses: 37 500 samples
up-sampling x2 required
At lowest speed (20 rpm)
◦ 450 000 samples collected
◦ Tach pulses: 112 500
Up-sampling x4 required
Up-sampling in the context of noise?
47. Thrust: Feature vectors are grouped according to
speed and a statistical model fit as function of
speed
Motivation: Effects of machinery resonances
managed by subdividing novelty detection
Limitations: Double curse of dimensionality,
assumption of Gaussianaity
Contribution:
◦ Application to real world (machinery) data
◦ Evaluated theoretical limitations with respect to
machinery
◦ Improved approach by suggesting whitening first
followed by normal novelty detection
48. Variable speed machinery
◦ Elements of a machine‟s vibratory response are
assumed to have a strong relation to the speed of
the given machinery
Distribution for speeds:
◦ Means vary with speed *C30
◦ Variances vary with resonance response
*C20
y
* C10
x
50. Thrust: One mode is included in the feature vector
which are grouped into bins according to ranges of
other mode (then employ multi-novelty detector
dispatch)
Motivation: Advance the technique to higher modes
Limitations: Curse of dimensionality, large number of
modes impractical, brute force
Contribution:
◦ Very practical technique compared to literature (for load
and speed)
◦ “Crossing” modes to enhance classification results
Experimental Data: Laurentian‟s TVS
Status: Not yet validated
51. Approach so far only works with one mode
Employ Timusk‟s novelty detector dispatch
technique
◦ Routine
Segment data into load bins
For each load bin build a uni-modal novelty detector
for all speed data in that load bin
◦ Improve results
Also build multiple detectors but based on speed bins
Combine classification results
52. Averaging modes still a problem
◦ Employ previous improvements
Curse of dimensionality increases
◦ Some mitigation possible
Brute force
◦ Across of the spectrum of techniques, not as bad as
parzen windowing (enter dataset is memorized)
Higher number of modes increases
computational complexities and curse of
dimensionality
53.
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75. Must account for speed!
Worden‟s Statistical Parameterization
◦ Good results
◦ Subject to double curse of dimensionality and
gaussianaity
Multi-Modal Novelty Detection
◦ Results on par or better than Worden‟s
◦ Somewhat insensitive to double curse of dimensionality
Feature vectors
◦ Statistics poor
Consequently, AE poor
◦ AR models produced excellent results
◦ Order Tracking poor
Why?
76. Thesis
◦ Multi-Modal Novelty Detection for Higher No.
Modes
◦ System Identification
No need to account for modes in novelty detection
Curse of dimensionality?
◦ Cross-Correlation
No need to measure modes
Silver bullet?
◦ Software Architecture
77. CEMI
Dr. Mechefske (Queens)
Dr. Timusk
Greg Lakanen
Greg Dalton