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LOGO
Condition Monitoring of Machinery Subject to Variable States
Condition Monitoring of Machinery Subject to Variable States
Jordan McBain, P.Eng.
Monitoring of Mobile Underground Mining Equipment
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Acknowledgments
Vale IncoCEMIDr. TimuskCommittee, External/Internal Reviewers
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Problem Overview
Maintenance options have 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.
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Problem Definition
Enable condition monitoring (CM) of mobile underground mining machinery Multiple Artificial Intelligence (AI) techniques
• As validated on laboratory test bench – gearbox and bearing faults
• Extensible to real-world applications?
Administration issues of automated computerized monitoring systems• Sporadic network availability• Bandwidth limited environments• Enterprise level integration
Extensible Software Engineering Architecture
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Outline
Background Condition Monitoring (CM) Artificial Intelligence (AI) for CM Monitoring of Variable-State Machinery
Methodology and Limitations Statistical Parameterization Augmented Novelty Detection System Identification Cross-Correlation Software Architecture Conclusion
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Background
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Maintenance Management
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
• Only 15% of failures follow MTBF model (Lihovd, 1998)– Naval/air study
Predictive Maintenance• Maintenance is delayed until some monitored parameter of the
equipment becomes erratic• Proactive• Balances resources
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Condition Monitoring
Thrust State of equipment determined by variations in
monitored parameters
Benefits Environment Safety Production Staff Shortages/Costs Scheduling Spare Parts (JIT) Insurance Life Extension
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AI for CM
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
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Pattern Recognition
One branch of AI domainPatterns used to compute decision ruleGeneralization(Double) Curse of Dimensionality
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Pattern Recognition
Sensing• Accelerometers, tachometers, acoustic emission
sensors, thermocouples, etc.
Segments• Choose time intervals for division of data• Synchronous intervals (fixed # of samples)• Asynchronous intervals (fixed # of shaft rotations)
Feature Extraction• N Parameters combined to form “patterns” or “feature vectors”• Statistics, Auto-regressive (AR) models, MUSIC Spectrum, etc.
Classify
• Generate decision rules from training data• Apply decision rules to test data• Fault detection: Novelty Detection (support vectors, neural
networks, etc.)
Post-Processing
• Diagnostics, prognostics• Health reporting• Sensor failure analysis• Etc.
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Monitored Parameters
Vibration, Thermography, Oil Analysis, NDT, et
Vibration Heavily used in literature Non-destructive, online,
sensitive Faults in rotating machinery
have strongly representative features in the frequency domain
Diagram: (Randall, 2004)
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Novelty Detection
Motivation: addresses imbalance of data from one class in relation to that of others Data from faulted states are difficult to collect (economics,
operations)
Sub problem of pattern recognition train on the “normal” class and then signal error when
behaviour deviates from the decision boundary
A wide variety of techniques available Examine two:
Boundaries containing a certain quantile of data (i.e. a statistical discordance test)
Boundaries derived by Support Vectors
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Support Vectors
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
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Variable-State Machinery
Primary aggravators: load and speed Referred to as nuisance variables in the literature (Gelman,
2005)
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 mechanical state (speed,
load, etc.)
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• 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 machine
Diagram: (Stack, 2003)
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Methodology and Limitations
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Test Bench
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Limitations
Test bench realism Mass of shaft
• Inertia of rotor system– Signal-to-Noise Ratio (SNR) of fault signals
Gear type• Helical gears
– Increased mesh strength» SNR of fault signals
Lack of complexity• Variable Frequency Drive (VFD) and/or particle break
control on load/speed– Torsional vibrations (typical in diesel engines) not
evaluated
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Limitations
Challenging control problem Closed loop on speed
VFD Open loop on load VFD Torque profile fed forward
to speed VFD Torque control
superimposed on speed control
Noisy torque signal Inconsistent effect on
algorithms
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Limitations
Applicability to Underground Environment Harsh conditions not present in laboratory Temperatures
• Degradation of lubricants• Thermal expansion of components
– Alters vibratory signature– Time-varying parameter not considered
Heavy shock/vibration• Noise for vibration-based CM
– Inclusion in training » Overly broad decision boundary
– Exclusion» Additionally processing required
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Understanding Classification Results
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Statistical Parameterization
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Statistical Parameterization
Established technique from the literature (Worden, 2001)
Motivation: Distribution of vibration parameters will change
according to time-varying parameters
Experiments with variable speed only
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Statistical Parameterization
Established Thrust: Develop a decision boundary that changes according to
speed Double Curse of Dimensionality Restrictive Gaussian assumption
x
y
* C10
*C20
*C30
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Statistical Parameterization
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Statistical Parameterization: Improvement
Contribution: Develop a rule to first center and whiten data
• Eigenvalue problem Center/whiten all training data
• Train SVDD Center/whiten test data according to rule
• Apply SVDD decision boundary to determine faults
x
y
* C10
*C20
*C30
x
y
Healthy Data for all Speeds
Faulted Data
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Statistical Parameterization
Choice of AR Model Order with Standard Statistical Parameterization(Interpolation)
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Statistical Parameterization
Statistical Paramterization with Whitening
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Statistical Parameterization
Interpolation over (4 consecutive) missing bins
Smaller number of missing bins Minimal impact
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Statistical Parameterication
Curse of Dimensionality Measured by increasing feature vector dimension
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Statistical Parameterization
Established approach Double curse of dimensionality Gaussian Assumption Excellent classification results
Statistical Parameterization with Whitening Mitigates double curse Provides more flexible boundary
• Reducing effect of Gaussian Assumption
Classification results at least as good
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Augmented Novelty Detection
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Augmented Novelty Detection
Previous limitations Varying degrees of curse of dimensionality Gaussian Assumption
Motivation Intuition gained from Statistical Parameterization
• Include time-varying parameter in feature vector– Trivial but not established in the literature
Problem reduced to standard novelty detection
Experiments with variable speed only
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Background: Order Tracking
Ordinarily: Vibration sampled at constant intervals Order tracking: vibration sampled at constant shaft
rotational intervals Use pulse train from tachometer to indicate sampling
interval Irregular resampling
Question: How many samples per shaft rotation are
appropriate to gain good classification results?
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Sensitivity Analysis: Order Tracking
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Order Tracking
Statistics
AR10
With OT Without OT
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Interesting Feature Vector: Acoustic Emissions
Statistical ParameterizationMulti-Modal Novelty Detection
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Baseline: Statistical Parameterization
AR10 Feature VectorStatistical Features
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Results
Statistical Parameterization Multi-Modal Novelty Detection
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Curse of Dimensionality
Multi-Modal ND Statistical Paramterization
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Validation: Experimental Procedure
Procedure:- Train with on one healthy gear- Validate on a different healthy gear and faulted components
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System Identification
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System Identification
Shifts problem to the feature vector rather than adapting decision boundary
Feature vector composed of elements of a gear’s transfer functions
Analysis with both varying speed and load
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System Identification
Assume a gear can be modeled as a torsional spring
Use system identification to model the transfer function with MIMO
System:
Gearbox Speed
LoadVibration
( )mx cx kx f t
x x u
y x u
B
C D
A 1 2
1 2
( ) ( )( ) ( ) ( )
( ) ( )
B z B zV z S z T z
A z A z
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Omitting Time-Varying Parameters
No Adaptation for Speed or Load No Adaptation for Load
Employing Multi-Modal Novelty Detection
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Sensitivity Analysis: Model Order
Changing Number of ZeroesChanging Number of Poles
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Curse of Dimensionality
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Generalization
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Cross-Correlation Analysis
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Cross-Correlation Analysis
SysID Failings Must measure all time-varying parameters Must develop transfer functions for each
• Susceptibility to the double curse of dimensionality?
Computational expensive
Cross-correlation based feature vector Sensors on disparate machinery components will
behave in a time-correlated manner Use statistical correlation signal
• Generate feature vectors from it
Eliminates failings of SysID*
( )[ ] [ ] [ ]m
f g n m g n mf
*( )( ) ( ) ( )f g t g t df
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Results
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Curse of Dimensionality
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Generalization
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Software Engineering Architecture
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Challenge
No silver bullet for condition monitoring (pattern recognition) Multitude of techniques for multitude of problems Wide variety of (transient) machinery Similar CM problems: prognostics, sensor failure analysis Extensible beyond rotating machinery
Pattern recognition problem generates multiple possible combinations of Sensing Segmentation Feature vector generation Classification techniques Post-processing requirements
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Software Design
Design for change! Recognize
broader-scoped problem• Intelligent
Signal Processing and Analysis
Smart Signal Processing
Structural Monitoring
Aircraft Monitoring
Stationary Equipment Monitoring
Seismicity Monitoring in Mines
Wind Turbine Monitoring
Ship Propulsion and Auxiliary System
Biomedical Monitoring
Automotive Part and Test Bench Monitoring
Vehicle Monitoring
Process Monitoring
Measure while Drilling
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Scope of Present Work
Design Object-Oriented (OO) Data Processing Layer Online, flexible and dynamic routing of signals Augmentable with user/programmer defined
techniques Design for intelligent signal processing
• Implement for CM
Create MATLAB prototypeReview and make recommendations for
integration with mining enterprise systems International Rock Excavation Data Exchange
Standard (IREDES)
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Use Cases
Hand-held, portable monitoring system Cheaper, economies of scale Intermittent monitoring
Dedicated online monitoring system Costly Equivalent problem to intermittent monitoring
• Intermittent functionality/benefits achievable by “wheeling” this system around
Capabilities to monitor more than one (physically proximate) machine at a time
Data Connectivity Limited bandwidth Intermittent network connectivity
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Design
Dynamic online signal routing Supports online selection of algorithms Subscription based
Multiple data sources From
• disc • DAQ • networked sensors
Varied sensor types Support n-dimensional signals
+MUserSamplesQueue()+register() : RegistrationToken+hasBeenCleared() : bool+addToQueue()+clearData()+getData()+unregister()
-Data-Time-AbsoluteTime
MUserSamplesQueue
+DataSource()+MonitorChannel() : RegistrationToken+getData()+clearData()+updateQueue()
-ChannelQueues : MUserSamplesQueue-SampleRatesArray-ChannelNames-updateQueues
DataSource
1 *
NetworkedSource AsynchronousDataSource StoredSensorDataDAQSystem
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Design
Signal conditioning strategy Typical signal processing techniques “Signal” representing time intervals for segmentation
Signal conditioner Does the actual work of
• getting sensor data • passing it through selected algorithms
Feature generator Requests conditioned signals from conditioner Segments signals according to segmentation strategy Combines multiple feature vectors into one
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Design
+DataSource()+MonitorChannel() : RegistrationToken+getData()+clearData()+updateQueue()
-ChannelQueues : MUserSamplesQueue-SampleRatesArray-ChannelNames-updateQueues
DataSource
-SegStrategy-SigConditioner-Name
FeatureGenerator
+SegmentationStrategy()+getSegmentTiming()
SegmentationStrategy
+SignalCondionter()+getConditionedData()+register()+unregister()+clearData()-recurseForConditionedData()-recurseRegister()-recurseClearData()
-SigConditoningChain : SignalConditioner
SignalConditioner
«uses»
«uses»
1
*
keyPhasor constantNumRotations constantTimeInterval«uses»
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Design
Intelligent Analyzer Strategy Requests feature
vectors from feature generator
Does the classification work• Depending on
“state” of classification problem
+DataSource()+MonitorChannel() : <unspecified>+getData()+clearData()+updateQueue()
-ChannelQueues-SampleRatesArray-ChannelNames-updateQueues
DataSource
-SegStrategy-SigConditioner-Name
FeatureGenerator
IntelligentAnalyzerStrategy
ExpertSystem FaultTree PatternRecognition
NoveltyDetection
SVDD
1
*
CombinationOfClassifiers
StatisticalParameterization
1 *
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Integration with Enterprise Layer
IREDES in need of augmentation for CM CM standards already exist
Don’t reinvent the wheel
Two options of differing granularity Open Systems Architecture for Enterprise Application
Integration (OSA-EAI) Open Systems Architecture for Condition-Base
Maintenance (OSA-CBM)
Wide industrial support US Navy Caterpillar Rockwell Automation Systems
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Conclusion
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Conclusions
No silver bullet for CM Wide variety of techniques for a wide variety of
applications Advances in CM for variable-state machinery
• Must consider time-varying parameters to optimize operations
Techniques Limitations:
• Normal Distribution• Double Curse of Dimensionality• Sensors to measure time-varying parameters
Extensible to other mining and non-mining applications
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Conclusions
Software architecture Recognize broader problem of Intelligent Signal Processing
• Subsumes CM, prognostics, sensor failure, etc. Design for change
• Greater breadth of marketability• Extensibility/Maintainability of Software Design
Integration at the Enterprise level• Rich standard exists to augment IREDES
Future work Take the solutions to the underground environment Validate in harsh environment
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