Data-Driven Technology for Engineering Systems Health Management: Design Approach, Feature Construction, Fault Diagnosis, Prognosis, Fusion and Decisions

364 Pages·2017·11.932 MB·English

by  Gang Niu (auth.)

Gang Niu
Data-Driven
Technology for
Engineering Systems
Health Management
Design Approach, Feature Construction,
Fault Diagnosis, Prognosis, Fusion and
Decisions
Data-Driven Technology for Engineering Systems
Health Management
Gang Niu
Data-Driven Technology
for Engineering Systems
Health Management
Design Approach, Feature Construction, Fault
Diagnosis, Prognosis, Fusion and Decision
123
Gang Niu
Institute of Rail Transit
TongjiUniversity
Shanghai
China
ISBN978-981-10-2031-5 ISBN978-981-10-2032-2 (eBook)
DOI 10.1007/978-981-10-2032-2
Jointlypublished with Science Press, Beijing,China
LibraryofCongressControlNumber:2016946012
©SpringerScience+BusinessMediaSingaporeandSciencePress,Beijing,China2017
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Preface
Systems health management (SHM) has emerged over recent years as significant
technologies that are making an impact on both military and commercial mainte-
nance practices. This discipline links studies of failure mechanisms to system
lifecycle management and is often referred to as prognostics and health manage-
ment (PHM), or in transportation applications—vehicle health management
(VHM).TechnicalapproachestobuildingmodelsinSHM/PHMcanbecategorized
broadly into data-driven approach, model-based approach, and hybrid approach.
The data-driven approach for SHM/PHM is also explained in condition-based
maintenance (CBM). CBM can be applied as a technical architecture and engi-
neering strategy of data-driven PHM. In this book, data-driven PHM/CBM is
introduced in details, which mainly emphasis functions of condition monitoring,
fault diagnosis, and prognosis.
Condition monitoring, fault diagnosis, and prognosis of engineering systems
havereceivedconsiderableattention inrecentyearsandareincreasinglybecoming
important in industry because of the need to increase reliability and decrease
possible loss of production due to faulty systems. Early fault detection, diagnosis,
and prognosis can increase system availability and performance, reduce conse-
quential damage, prolong machine life, and reduce spare parts inventories and
breakdownmaintenance.Withthedevelopmentofartificialintelligencetechniques,
many intelligent systems have been employed to assist the maintenance manage-
ment task to correctly interpret the fault data.
This book aims to provide latest research findings and advanced techniques for
the fault diagnosis and prognosis area of engineering systems. It introduces the
developmentsandapplicationsofintelligentdiagnosisandprognosistechniquesin
recent years.
Thisbookdetailsthetechniqueforintelligent faultdiagnosisandprognosisthat
implements data-driven approach. Data-driven methodology consists of data
acquisition, feature extraction, feature selection, classification, prognosis and data
fusion algorithms, etc. for decision making. Each step of data-driven strategy is
reviewed including examples. It provides a foundation in the data acquisition,
v
vi Preface
analysis, feature extraction and selection, classification of equipment faults and
prognosis through text discussion, worked examples, applications, and use of
modern computer tools. Most chapters include with examples showing how to use
these tools to solve situations not easily amenable to analytic solutions. This book
provides practice in identifying, formulating, and solving fault diagnosis and
prognosisproblems.Anextensivesetofworkedexamplesofferstheopportunityto
apply concepts discussed in the book to analyze and solve a variety of problems.
The organization and basic subject matter for this book parallel a 17 intensive
course entitled “Engineering Systems Health Management” offered by Dr. Gang
Niu at Tongji University.
Shanghai, China Gang Niu
Acknowledgements
The integration task needed to pull together cohesive and up-to-date materials was
enormousandwouldnothavebeenpossible withouttheunselfishandenthusiastic
supportIreceivedfrommypreviouscolleaguesandcoworkers.Theresearchwork
offaculty andstudentsattheIntelligentMechanicalLaboratory(IML)ofPukyong
NationalUniversityandtheCenterforAdvancedLifeCycleEngineering(CALCE)
ofUniversityofMarylandformthebackboneofmanytopicscoveredinthisbook.
Special thanks also to the Intelligent Control Systems Laboratory of the Georgia
Institute of Technology and the Center for Intelligent Maintenance Systems
(IMS)ofUniversityofCincinnatifor valuable references.Theauthorisgratefulto
Achmad Widodo, Van Tung Tran, Tian Han, Martha Arbayani Bin Zaidan, and
Jing Tian, among others for their valuable contributions.
I particularly thank mygraduate students for helping collect and integrate some
of the original draft.
I am gratefully acknowledging the support received over the past years from
many industrial sponsors. It is impossible to acknowledge by name many
researchers whose work referred in this book.
TheworkpresentedinthisbookwassupportedbytheNationalNaturalScience
Foundation of China (Grant No. 51575396 and 51205291).
Finally, I would like to express my gratitude to my parents, my wife, and my
little son James, for their patience, understanding, and encouragement during the
hard creation of this book.
Gang Niu
vii
Contents
1 Background of Systems Health Management . .... .... ..... .... 1
1.1 Introduction .. .... ..... .... .... .... .... .... ..... .... 1
1.2 Maintenance Strategy.... .... .... .... .... .... ..... .... 3
1.3 From Maintenance to PHM ... .... .... .... .... ..... .... 10
1.4 Definitions and Terms of Systems Health Management ... .... 12
1.5 Preface to Book Chapters. .... .... .... .... .... ..... .... 13
References. .... .... .... ..... .... .... .... .... .... ..... .... 14
2 Design Approach for Systems Health Management .... ..... .... 15
2.1 Introduction .. .... ..... .... .... .... .... .... ..... .... 15
2.2 Systems Engineering..... .... .... .... .... .... ..... .... 17
2.3 Systems Engineering, Dependability, and Health
Management.. .... ..... .... .... .... .... .... ..... .... 18
2.4 SHM Lifecycle Stages ... .... .... .... .... .... ..... .... 20
2.4.1 Research Stage .. .... .... .... .... .... ..... .... 20
2.4.2 Requirements Development Stage.... .... ..... .... 21
2.4.3 System/Functional Analysis. .... .... .... ..... .... 23
2.4.4 Design, Synthesis, and Integration.... .... ..... .... 25
2.4.5 System Test and Evaluation .... .... .... ..... .... 27
2.4.6 HM System Maturation.... .... .... .... ..... .... 29
2.5 A Systems-Based Methodology for PHM/CBM Design... .... 30
2.6 A Proposed PHM Design Approach for Rotary Machinery
Systems . .... .... ..... .... .... .... .... .... ..... .... 32
References. .... .... .... ..... .... .... .... .... .... ..... .... 33
3 Overview of Data-Driven PHM .... .... .... .... .... ..... .... 35
3.1 Introduction .. .... ..... .... .... .... .... .... ..... .... 35
3.2 PHM Technical Approaches... .... .... .... .... ..... .... 37
3.3 Data-Driven PHM/CBM System Architecture.. .... ..... .... 39
3.4 Role of Condition Monitoring, Fault Diagnosis,
and Prognosis. .... ..... .... .... .... .... .... ..... .... 40
3.5 Fault Diagnosis Framework ... .... .... .... .... ..... .... 41
ix
x Contents
3.6 Problems During Implementation... .... .... .... ..... .... 43
3.7 Related Techniques. ..... .... .... .... .... .... ..... .... 45
References. .... .... .... ..... .... .... .... .... .... ..... .... 47
4 Data Acquisition and Preprocessing. .... .... .... .... ..... .... 49
4.1 Introduction .. .... ..... .... .... .... .... .... ..... .... 49
4.2 Data Acquisition... ..... .... .... .... .... .... ..... .... 50
4.2.1 Selecting a Proper Measure. .... .... .... ..... .... 51
4.2.2 Vibration Transducers. .... .... .... .... ..... .... 52
4.2.3 Transducer Selection.. .... .... .... .... ..... .... 58
4.2.4 Transducer Mounting.. .... .... .... .... ..... .... 59
4.2.5 Transducer Location .. .... .... .... .... ..... .... 63
4.2.6 Frequency Span.. .... .... .... .... .... ..... .... 65
4.2.7 Data Display.... .... .... .... .... .... ..... .... 66
4.3 Data Processing ... ..... .... .... .... .... .... ..... .... 70
4.4 Data Analysis. .... ..... .... .... .... .... .... ..... .... 85
4.4.1 Features in Time Domain .. .... .... .... ..... .... 85
4.4.2 Features in Frequency Domain .. .... .... ..... .... 89
4.4.3 Features in Time–Frequency Domain . .... ..... .... 94
References. .... .... .... ..... .... .... .... .... .... ..... .... 98
5 Statistic Feature Extraction ... .... .... .... .... .... ..... .... 101
5.1 Introduction .. .... ..... .... .... .... .... .... ..... .... 101
5.2 Basic Concepts.... ..... .... .... .... .... .... ..... .... 105
5.2.1 Pattern and Feature Vector . .... .... .... ..... .... 105
5.2.2 Class. .... ..... .... .... .... .... .... ..... .... 105
5.3 Parameter Evaluation Technique.... .... .... .... ..... .... 106
5.4 Principal Component Analysis (PCA).... .... .... ..... .... 107
5.5 Independent Component Analysis (ICA) . .... .... ..... .... 109
5.6 Kernel PCA .. .... ..... .... .... .... .... .... ..... .... 112
5.7 Kernel ICA... .... ..... .... .... .... .... .... ..... .... 113
5.8 Fisher Discriminant Analysis (FDA). .... .... .... ..... .... 115
5.9 Linear Discriminant Analysis (LDA) .... .... .... ..... .... 117
5.10 Generalized Discriminant Analysis (GDA).... .... ..... .... 121
5.11 Clustering.... .... ..... .... .... .... .... .... ..... .... 125
5.11.1 k-Centers Clustering .. .... .... .... .... ..... .... 128
5.11.2 k-Means Clustering ... .... .... .... .... ..... .... 130
5.11.3 Hierarchical Clustering .... .... .... .... ..... .... 132
5.12 Other Techniques .. ..... .... .... .... .... .... ..... .... 136
References. .... .... .... ..... .... .... .... .... .... ..... .... 137
6 Feature Selection Optimization. .... .... .... .... .... ..... .... 139
6.1 Introduction .. .... ..... .... .... .... .... .... ..... .... 139
6.2 Individual Feature Evaluation (IFE) . .... .... .... ..... .... 141
6.3 Conditional Entropy ..... .... .... .... .... .... ..... .... 143
6.4 Backward Feature Selection ... .... .... .... .... ..... .... 145
Contents xi
6.5 Forward Feature Selection .... .... .... .... .... ..... .... 146
6.6 Branch and Bound Feature Selection .... .... .... ..... .... 148
6.7 Plus l-Take Away r Feature Selection ... .... .... ..... .... 153
6.8 Floating Forward Feature Selection . .... .... .... ..... .... 155
6.9 Distance-Based Evaluation Technique ... .... .... ..... .... 158
6.10 Taguchi Method-Based Feature Selection. .... .... ..... .... 159
6.11 Genetic Algorithm . ..... .... .... .... .... .... ..... .... 161
6.11.1 General Concept . .... .... .... .... .... ..... .... 161
6.11.2 Differences from Other Traditional Methods..... .... 164
6.11.3 Simple Genetic Algorithm (SGA) .... .... ..... .... 165
6.11.4 Feature Selection Using GA .... .... .... ..... .... 166
6.12 Summary .... .... ..... .... .... .... .... .... ..... .... 169
References. .... .... .... ..... .... .... .... .... .... ..... .... 171
7 Intelligent Fault Diagnosis Methodology . .... .... .... ..... .... 173
7.1 Introduction .. .... ..... .... .... .... .... .... ..... .... 173
7.2 Linear Classifier ... ..... .... .... .... .... .... ..... .... 174
7.2.1 Linear Separation of Finite Set of Vectors.. ..... .... 175
7.2.2 Perceptron Algorithm . .... .... .... .... ..... .... 176
7.2.3 Kozinec’s Algorithm.. .... .... .... .... ..... .... 177
7.2.4 Multi-class Linear Classifier .... .... .... ..... .... 178
7.3 Quadratic Classifier. ..... .... .... .... .... .... ..... .... 179
7.4 Bayesian Classifier. ..... .... .... .... .... .... ..... .... 181
7.5 k-Nearest Neighbors (k-NN)... .... .... .... .... ..... .... 182
7.6 Self-Organizing Feature Map (SOFM) Neural Network ... .... 183
7.7 Learning Vector Quantization (LVQ) Neural Network.... .... 187
7.8 Radial Basis Function (RBF) Neural Network . .... ..... .... 190
7.9 ART Kohonen Neural Network (ART-KNN).. .... ..... .... 192
7.10 Support Vector Machines (SVMs) .. .... .... .... ..... .... 196
7.10.1 Wavelet SVM ... .... .... .... .... .... ..... .... 200
7.10.2 Multi-class Classification... .... .... .... ..... .... 203
7.10.3 Sequential Minimal Optimization (SMO) .. ..... .... 205
7.11 Decision Tree. .... ..... .... .... .... .... .... ..... .... 209
7.11.1 Building Decision Tree.... .... .... .... ..... .... 210
7.11.2 Pruning Decision Tree. .... .... .... .... ..... .... 214
7.12 Random Forest.... ..... .... .... .... .... .... ..... .... 216
7.12.1 Random Forest .. .... .... .... .... .... ..... .... 217
7.12.2 Random Forest Algorithm (RF).. .... .... ..... .... 219
7.12.3 Genetic Algorithm.... .... .... .... .... ..... .... 223
7.13 Adaptive Neurofuzzy Integrated System (ANFIS) .. ..... .... 225
7.13.1 Classification and Regression Tree (CART) ..... .... 226
7.13.2 Adaptive Neurofuzzy Inference System (ANFIS) . .... 229