Machinery Prognostics and Prognosis Oriented Maintenance Management (eBook)

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.

Preface i

Acknowledgements i

Chapter 1 Introduction 7

1.1 Historical perspective 7

1.2 Diagnostic and prognostic system requirements 8

1.3 Need for prognostics and sustainability based maintenance management 9

1.4 Technical challenges in prognosis and sustainability based maintenance decision making 11

1.5 Data processing, prognostics and decision making 13

1.6 Sustainability based maintenance management 16

1.7 Future of prognostics based maintenance 19

References 20

Chapter 2 Data processing 21

2.1 Probability Distributions 21

2.2 Statistics on Unordered data 32

2.3 Statistics on Ordered Data 38

2.4 Technologies for incomplete data 39

References 428

Chapter 3 Signal processing 45

3.1 Introduction 45

3.2 Signal pre-processing 47

3.3 Techniques for signal processing 50

3.4 Real-time image feature extraction 72

3.5 Fusion or integration technologies 77

3.6 Statistical pattern recognition and data mining 80

3.7 Advanced technology for feature extraction 92

References 102

Chapter 4 Health monitoring and prognosis 110

4.1 Health monitoring as a concept 110

4.2 Degradation indices 111

4.3 Real-time monitoring 116

4.4 Failure prognosis 142

4.5 Physics-based prognosis models 155

4.6 Data-driven prognosis models 158

4.7 Hybrid prognosis models 162

Reference 165

Chapter 5 Prediction of residual service life 172

5.1 Formulation of problem 172

5.2 Methodology of probabilistic prediction 173

5.3 Dynamic life prediction using time series 180

5.4 Residual life prediction by crack-growth criterion 197

References 202

Chapter 6 Maintenance planning and scheduling 205

6.1 Strategic planning in maintenance 205

6.2 Maintenance scheduling 219

6.3 Scheduling techniques 232

6.4 Heuristic methodology for multi-unit system maintenance scheduling 261

References 266

Chapter 7 Prognosis incorporating maintenance decision making 270

7.1 The changing role of maintenance 270

7.2 Development of maintenance 272

7.3 Maintenance effects modeling 274

7.4 Modeling of optimization objective - maintenance cost 282

7.5 Prognosis oriented maintenance decision making 284

7.6 Maintenance decision making considering energy consumption 301

References 317

Chapter 8 Case studies 321

8.1 Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis 322

8.2 Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery 329

8.3 BP Neural Networks Based Prognostic Methodology and Its Application 336

8.4 A Dynamic Multi-scale Markov Model Based Methodology for Remaining Life Prediction 343

8.5 A group technology based methodology for maintenance scheduling for hybrid shop 358

References 365

Index 369