A Guide to Understanding Machinery Prognostics and Prognosis Oriented Maintenance Management
Will be published approx. on 23. January 2015
Book
Hardback
360 pages
978-1-118-63872-9 (ISBN)
Description
This book gives a complete presentatin of the basic essentials of machinery prognostics and prognosis oriented maintenance management, and takes a look at the cutting-edge discipline of intelligent failure prognosis technologies for condition-based maintenance.
* Presents an introduction to advanced maintenance systems, and discusses the key technologies for advanced maintenance by providing readers with up-to-date technologies
* Offers practical case studies on performance evaluation and fault diagnosis technology, fault prognosis and remaining useful life prediction and maintenance scheduling, enhancing the understanding of these technologies
* Pulls togeter recent developments and varying methods into one volume, complemented by practical examples to provide a complete reference
More details
Other editions
Additional editions
E-Book
12/2014
Wiley
€118.99
Available for download
E-Book
11/2014
Wiley
€118.99
Available for download
Person
Jihong Yan, Professor and Head of Department of Industrial Engineering, Harbin Institute of Technology, China
Professor Yan has been working in the area of intelligent maintenance for over ten years, starting at the Centre for Intelligent Maintenance Systems (IMS) funded by NSF in the US as a researcher for three years, mainly focused on prognosis algorithm development. He then joined Pennsylvania State University in 2004 to work on personnel cross training related topics. From 2005 to the present he is a Professor at Harbin Institute of Technology, China. Professor Yan's research is focused on advanced maintenance of machinery, such as online condition monitoring, signal data pre-processing, feature extraction, reliability and performance evaluation, fault diagnosis, fault prognosis and remaining useful life prediction, and maintenance scheduling.
Professor Yan has been working in the area of intelligent maintenance for over ten years, starting at the Centre for Intelligent Maintenance Systems (IMS) funded by NSF in the US as a researcher for three years, mainly focused on prognosis algorithm development. He then joined Pennsylvania State University in 2004 to work on personnel cross training related topics. From 2005 to the present he is a Professor at Harbin Institute of Technology, China. Professor Yan's research is focused on advanced maintenance of machinery, such as online condition monitoring, signal data pre-processing, feature extraction, reliability and performance evaluation, fault diagnosis, fault prognosis and remaining useful life prediction, and maintenance scheduling.
Content
About the Author xi
Preface xiii
Acknowledgements xv
1 Introduction 1
1.1 Historical Perspective 1
1.2 Diagnostic and Prognostic System Requirements 2
1.3 Need for Prognostics and Sustainability-Based Maintenance Management 3
1.4 Technical Challenges in Prognosis and Sustainability-Based Maintenance Decision-Making 4
1.5 Data Processing, Prognostics, and Decision-Making 7
1.6 Sustainability-Based Maintenance Management 9
1.7 Future of Prognostics-Based Maintenance 11
References 12
2 Data Processing 13
2.1 Probability Distributions 13
2.1.1 Uniform Distribution 14
2.1.2 Normal Distribution 16
2.1.3 Binomial Distribution 18
2.1.4 Geometric Distribution 19
2.1.5 Hyper-Geometric Distribution 21
2.1.6 Poisson Distribution 22
2.1.7 Chi-Squared Distributions 24
2.2 Statistics on Unordered Data 25
2.2.1 Treelets Analysis 26
2.2.2 Clustering Analysis 28
2.3 Statistics on Ordered Data 32
2.4 Technologies for Incomplete Data 33
References 34
3 Signal Processing 37
3.1 Introduction 37
3.2 Signal Pre-Processing 38
3.2.1 Digital Filtering 38
3.2.2 Outlier Detecting 39
3.2.3 Signal Detrending 41
3.3 Techniques for Signal Processing 42
3.3.1 Time-Domain Analysis 42
3.3.2 Spectrum Analysis 44
3.3.3 Continuous Wavelet Transform 46
3.3.4 Discrete Wavelet Transform 49
3.3.5 Wavelet Packet Transforms 51
3.3.6 Empirical Mode Decomposition 51
3.3.7 Improved Empirical Mode Decomposition 57
3.4 Real-Time Image Feature Extraction 67
3.4.1 Image Capture System 67
3.4.2 Image Feature Extraction 68
3.5 Fusion or Integration Technologies 72
3.5.1 Dempster-Shafer Inference 72
3.5.2 Fuzzy Integral Fusion 73
3.6 Statistical Pattern Recognition and Data Mining 74
3.6.1 Bayesian Decision Theory 74
3.6.2 Artificial Neural Networks 76
3.6.3 Support Vector Machine 79
3.7 Advanced Technology for Feature Extraction 85
3.7.1 Group Technology 87
3.7.2 Improved Algorithm of Group Technology 88
3.7.3 Numerical Simulation of Improved Group Algorithm 90
3.7.4 Group Technology for Feature Extraction 91
3.7.5 Application 92
References 96
4 Health Monitoring and Prognosis 101
4.1 Health Monitoring as a Concept 101
4.2 Degradation Indices 101
4.3 Real-Time Monitoring 106
4.3.1 Data Acquisition 106
4.3.2 Data Processing Techniques 115
4.3.3 Example 120
4.4 Failure Prognosis 126
4.4.1 Classification and Clustering 129
4.4.2 Mathematical Model of the Classification Method 130
4.4.3 Mathematical Model of the Fuzzy C-Means Method 130
4.4.4 Theory of Ant Colony Clustering Algorithm 133
4.4.5 Improved Ant Colony Clustering Algorithm 134
4.4.6 Intelligent Fault Diagnosis Method 138
4.5 Physics-Based Prognosis Models 141
4.5.1 Model-Based Methods for Systems 142
4.6 Data-Driven Prognosis Models 144
4.7 Hybrid Prognosis Models 147
References 149
5 Prediction of Remaining Useful Life 153
5.1 Formulation of Problem 153
5.2 Methodology of Probabilistic Prediction 154
5.2.1 Theory of Weibull Distribution 155
5.2.2 Bayesian Theorem 157
5.3 Dynamic Life Prediction Using Time Series 160
5.3.1 General Introduction 160
5.3.2 Prediction Models 162
5.3.3 Applications 173
5.4 Remaining Life Prediction by the Crack-Growth Criterion 176
References 181
6 Maintenance Planning and Scheduling 183
6.1 Strategic Planning in Maintenance 183
6.1.1 Definition of Maintenance 183
6.1.2 Maintenance Strategy Planning 188
6.2 Maintenance Scheduling 196
6.2.1 Fundamentals of Maintenance Scheduling 196
6.2.2 Problem Formulation 202
6.2.3 Models for Maintenance Scheduling 203
6.3 Scheduling Techniques 207
6.3.1 Maintenance Timing Decision-Making Method Based on MOCLPSO 207
6.3.2 Grouping Methods for Maintenance 214
6.3.3 Maintenance Scheduling Based on a Tabu Search 222
6.3.4 Dynamic Scheduling of Maintenance Measure 223
6.3.5 Case Study 229
6.4 Heuristic Methodology for Multi-unit System Maintenance Scheduling 231
6.4.1 Models or Multi-Unit System Maintenance Decision 232
6.4.2 Heuristic Maintenance Scheduling Algorithm 233
6.4.3 Case Study 234
6.4.4 Conclusions and Discussions 237
References 237
7 Prognosis Incorporating Maintenance Decision-Making 241
7.1 The Changing Role of Maintenance 241
7.2 Development of Maintenance 243
7.3 Maintenance Effects Modeling 244
7.3.1 Reliability Estimation 245
7.3.2 Modeling the Improvement of Reliability after Maintenance 247
7.4 Modeling of Optimization Objective - Maintenance Cost 251
7.5 Prognosis-Oriented Maintenance Decision-Making 253
7.5.1 Reliability Estimation and Prediction 253
7.5.2 Case Study 254
7.5.3 Maintenance Scheduling Based on Reliability Estimation and Prediction by Prognostic Methodology 260
7.5.4 Case Description 265
7.6 Maintenance Decision-Making Considering Energy Consumption 269
7.6.1 Energy Consumption Modeling 269
7.6.2 Implementation 273
7.6.3 Verification and Conclusions 279
References 284
8 Case Studies 287
8.1 Improved Hilbert-Huang Transform Based Weak Signal Detection Methodology and Its Application to Incipient Fault Diagnosis and ECG Signal Analysis 287
8.1.1 Incipient Fault Diagnosis Using Improved HHT 287
8.1.2 HHT in Low SNR Scenario 290
8.1.3 Summary 293
8.2 Ant Colony Clustering Analysis Based Intelligent Fault Diagnosis Method and Its Application to Rotating Machinery 293
8.2.1 Description of Experiment and Data 293
8.2.2 Model Training for Fault Diagnosis 294
8.2.3 Fault Recognition 298
8.2.4 Summary 300
8.3 BP Neural Networks Based Prognostic Methodology and Its Application 300
8.3.1 Experimental Test Conditions 301
8.3.2 BP Network Model Training 302
8.3.3 BP Network Real-Time Prognostics 304
8.3.4 Error Analysis for Prediction 305
8.3.5 PDF Curve for Life Prediction 305
8.3.6 Summary 307
8.4 A Dynamic Multi-Scale Markov Model Based Methodology for Remaining Life Prediction 307
8.4.1 Introduction 307
8.4.2 Methods of Signal Processing and Performance Assessment 308
8.4.3 Markov-Based Model for Remaining Life Prediction 309
8.4.4 Experiment and Validation 315
8.4.5 Summary 321
8.5 A Group Technology Based Methodology for Maintenance Scheduling for a Hybrid Shop 322
8.5.1 Introduction 322
8.5.2 Production System Modeling 322
8.5.3 Clustering-Based Grouping Method 323
8.5.4 Application 323
8.5.5 Summary 327
References 328
Index 331