Condition Monitoring of Variable State Machinery

LOGO

Condition Monitoring of
Machinery Subject to
Variable States

Monitoring of Mobile
Underground Mining Equipment
Jordan McBain, P.Eng.

1

Acknowledgments

Vale Inco
CEMI
Dr. Timusk
Committee, External/Internal Reviewers

2

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.

3

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
4

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

5

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
7

Condition Monitoring

Thrust
 State of equipment determined by variations in
monitored parameters
Benefits
 Environment
 Safety
 Production
 Staff Shortages/Costs
 Scheduling
 Spare Parts (JIT)
 Insurance
8  Life Extension

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

9

Pattern Recognition

One branch of AI domain
Patterns used to compute decision rule
Generalization
(Double) Curse of Dimensionality

10

Pattern Recognition

• Accelerometers, tachometers, acoustic emission sensors, thermocouples, etc.
Sensing

• Choose time intervals for division of data
• Synchronous intervals (fixed # of samples)
• Asynchronous intervals (fixed # of shaft rotations)
Segments

• N Parameters combined to form “patterns” or “feature vectors”
Feature • Statistics, Auto-regressive (AR) models, MUSIC Spectrum, etc.
Extraction

• Generate decision rules from training data
• Apply decision rules to test data
Classify • Fault detection: Novelty Detection (support vectors, neural networks, etc.)

• Diagnostics, prognostics
• Health reporting
Post- • Sensor failure analysis
Processing • Etc.

11

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)

12

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

13

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

14

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.)

15

• 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)

16

Methodology and Limitations

17

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

19

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
20 algorithms

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
21

Understanding Classification Results

22

Statistical Parameterization

23


Established technique from the literature
(Worden, 2001)
Motivation:
 Distribution of vibration parameters will change
according to time-varying parameters
Experiments with variable speed only

24


 Established Thrust:
 Develop a decision boundary that changes according to
speed
 Double Curse of Dimensionality *C30

 Restrictive Gaussian assumption

*C20
y

* C10

x

25


26

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

Faulted Data
*C30

y

*C20 Healthy Data
y
for all Speeds
x
* C10

x
27


Choice of AR Model Order with Standard Statistical Parameterization
(Interpolation)

28


Statistical Paramterization with Whitening

29


Interpolation over (4 consecutive) missing
bins

Smaller number of missing bins
 Minimal impact

30

Statistical Parameterication

Curse of Dimensionality
 Measured by increasing feature vector dimension

31


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

32

Augmented Novelty Detection

33

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

34

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?

35

Sensitivity Analysis: Order Tracking

36

Order Tracking
With OT Without OT

Statistics

AR10

37

Interesting Feature Vector: Acoustic
Emissions

Multi-Modal Novelty Detection Statistical Parameterization

38

Baseline: Statistical Parameterization

Statistical Features AR10 Feature Vector

39

Results

Statistical Parameterization Multi-Modal Novelty Detection

40

Curse of Dimensionality

Multi-Modal ND Statistical Paramterization

41

Validation: Experimental Procedure

Procedure:
- Train with on one healthy gear
- Validate on a different healthy gear and faulted components
42

System Identification

43


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

44


Assume a gear can be modeled as a
torsional spring
 
mx cx kx f (t )
Use system identification to model the
transfer function with MIMO

x Ax Bu B1 ( z ) B2 ( z )
V ( z) S ( z) T ( z)
y C x Du A1 ( z ) A2 ( z )

System:
Speed
Gearbox
Vibration
Load
45

Omitting Time-Varying Parameters

Employing Multi-Modal Novelty Detection
No Adaptation for Speed or Load No Adaptation for Load

46

Sensitivity Analysis: Model Order

Changing Number of Poles Changing Number of Zeroes

47


48

Cross-Correlation Analysis

50

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 ( f g )(t ) f ( ) g (t )d
 Eliminates failings of SysID *
( f g )[n] f [ m] g [ n m]
m
51


53

Software Engineering Architecture

55

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
56

Software Design

Structural
Monitoring
Measure Aircraft
while Drilling Monitoring

Design for Process
Monitoring
Stationary
Equipment
Monitoring

change!
 Recognize Smart
Signal
Processing Seismicity
broader- Vehicle
Monitoring
Monitoring
in Mines

scoped
problem Automotive
Wind
Part and Test
• Intelligent Bench
Monitoring
Turbine
Monitoring

Signal Ship
Biomedical Propulsion
Processing Monitoring and Auxiliary
System
and Analysis
57

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 prototype
Review and make recommendations for
integration with mining enterprise systems
 International Rock Excavation Data Exchange
58 Standard (IREDES)

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

59

Design

Dynamic online signal routing
 Supports online selection of algorithms
 Subscription based
MUserSamplesQueue
DataSource
-Data

Multiple data sources
-ChannelQueues : MUserSamplesQueue
-Time
-SampleRatesArray
-AbsoluteTime
-ChannelNames
-updateQueues +MUserSamplesQueue()
+register() : RegistrationToken

 From
+DataSource()
1 * +hasBeenCleared() : bool
+MonitorChannel() : RegistrationToken
+addToQueue()
+getData()
+clearData()
+clearData()
+getData()
• disc +updateQueue()
+unregister()

• DAQ NetworkedSource
DAQSystem AsynchronousDataSource StoredSensorData

• networked sensors
 Varied sensor types
 Support n-dimensional signals
60

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

61

Design

SignalConditioner
-SigConditoningChain : SignalConditioner
+SignalCondionter()
+getConditionedData() SegmentationStrategy
+register()
+unregister() «uses»
+clearData() +SegmentationStrategy()
-recurseForConditionedData() +getSegmentTiming()
-recurseRegister() «uses»
«uses»
-recurseClearData() keyPhasor constantNumRotations constantTimeInterval

1
FeatureGenerator
-SegStrategy
-SigConditioner
-Name

*

DataSource
-ChannelQueues : MUserSamplesQueue
-SampleRatesArray
-ChannelNames
-updateQueues
+DataSource()
+MonitorChannel() : RegistrationToken
+getData()
+clearData()
+updateQueue()

62

Design
FeatureGenerator
*

Intelligent
-SegStrategy
-SigConditioner
-Name

Analyzer Strategy 1

IntelligentAnalyzerStrategy
 Requests feature DataSource
-ChannelQueues

vectors from -SampleRatesArray
-ChannelNames
-updateQueues
feature generator +DataSource()
+MonitorChannel() : <unspecified>
+getData()

 Does the +clearData()
+updateQueue()
ExpertSystem FaultTree PatternRecognition

classification work
• Depending on 1 *

“state” of CombinationOfClassifiers NoveltyDetection

classification
problem

StatisticalParameterization SVDD

63

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

64

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
66 applications

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

67

Condition Monitoring of Variable State Machinery

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