Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance.
Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more.
Author(s): Hosameldin Ahmed, Asoke K. Nandi
Publisher: Wiley-IEEE Press
Year: 2019
Language: English
Pages: 414
Cover......Page 1
Condition Monitoring with Vibration Signals
......Page 3
© 2020......Page 4
Dedication......Page 5
Contents......Page 6
Preface......Page 15
About the Authors......Page 18
List of Abbreviations......Page 20
Part I:
Introduction......Page 27
1
Introduction to Machine Condition Monitoring......Page 28
2 Principles of Rotating Machine Vibration Signals......Page 41
Part II:
Vibration Signal Analysis Techniques......Page 55
3
Time Domain Analysis......Page 56
4 Frequency Domain Analysis......Page 85
5 Time-Frequency Domain Analysis......Page 100
Part III:
Rotating Machine Condition Monitoring Using Machine Learning......Page 136
6
Vibration-Based Condition Monitoring Using Machine Learning......Page 137
7 Linear Subspace Learning......Page 151
8 Nonlinear Subspace Learning......Page 172
9 Feature Selection......Page 192
Part IV:
Classification Algorithms......Page 218
10
Decision Trees and Random Forests......Page 219
11 Probabilistic Classification Methods......Page 243
12 Artificial Neural Networks (ANNs)......Page 256
13 Support Vector Machines (SVMs)......Page 276
14 Deep Learning......Page 295
15 Classification Algorithm Validation......Page 322
Part V:
New Fault Diagnosis Frameworks Designed for MCM......Page 335
16
Compressive Sampling and Subspace Learning (CS-SL)......Page 336
17 Compressive Sampling and Deep Neural Network (CS-DNN)......Page 373
18 Conclusion......Page 390
Appendix.
Machinery Vibration Data Resources and Analysis Algorithms......Page 399
References......Page 404
Index......Page 405
