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E-grāmata: Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems [Taylor & Francis e-book]

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Machine Learning-Based Fault Diagnosis for Industrial Engineering SystemsPalielināt
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This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery. Techniques and applications through experiments and case studies for intelligent precision compensation and fault diagnosis are offered along with the introduction of machine learning and deep learning methods.

Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems discusses how to formulate and solve precision compensation and fault diagnosis problems. The book includes experimental results on hardware equipment used as practical examples throughout the book. Machine learning and deep learning methods used in intelligent precision compensation and intelligent fault diagnosis are introduced. Applications to deal with relevant problems concerning CNC machining and rotating machinery in industrial engineering systems are provided in detail along with applications used in precision motion systems.

Methods, applications, and concepts offered in this book can help all professional engineers and students across many areas of engineering and operations management that are involved in any part of Industry 4.0 transformation.
Preface ix
Authors xi
Chapter 1 Background and Related Methods
1(10)
1.1 Background
1(1)
1.2 Related Methods
2(9)
1.2.1 Back Propagation Neural Network
2(1)
1.2.2 Convolutional Neural Network
3(1)
1.2.3 Recurrent Neural Network
4(1)
1.2.4 Generative Adversarial Networks
5(1)
1.2.5 Bagging Algorithm
5(1)
1.2.6 Classification and Regression Tree
6(1)
1.2.7 Random Forest
6(1)
1.2.8 Density-Based Spatial Clustering of Applications with Noise
7(1)
1.2.9 Safe-Level Synthetic Minority Over-Sampling Technique
8(1)
Bibliography
8(3)
Chapter 2 Fault Diagnosis Method Based on Recurrent Convolutional Neural Network
11(16)
2.1 Introduction
11(1)
2.2 Model Establishment and Theoretical Derivation
11(8)
2.2.1 One-Dimensional Convolutional Neural Network
12(1)
2.2.2 Convolutional Recurrent Neural Network Model
13(5)
2.2.3 Dropout in Neural Network Model
18(1)
2.3 Diagnostic Flow of the Proposed Method
19(1)
2.4 Experimental Research Based on The Proposed Method
20(7)
2.4.1 Experiment Platform
20(1)
2.4.2 Experimental Setup
21(1)
2.4.3 Summary of Experimental Results
22(1)
Bibliography
23(4)
Chapter 3 Fault Diagnosis of Rotating Machinery Gear Based on Random Forest Algorithm
27(12)
3.1 Introduction
27(1)
3.2 Fault Diagnosis of Rotating Machinery Gear Based on Random Forest Algorithm
28(2)
3.3 Experimental Verification
30(9)
3.3.1 Experiment Platform
31(2)
3.3.2 Experimental Results
33(2)
3.3.3 Comparison Study
35(2)
Bibliography
37(2)
Chapter 4 Bearing Fault Diagnosis under Different Working Conditions Based on Generative Adversarial Networks
39(8)
4.1 Introduction
39(1)
4.2 Model Establishment and Theoretical Derivation
40(4)
4.2.1 Wasserstein Generative Adversarial Network
40(1)
4.2.2 Maximum Mean Discrepancy
41(1)
4.2.3 Establishment of Fault Diagnosis Model
42(1)
4.2.4 Fault Diagnosis Procedures of the Proposed Method
43(1)
4.3 Experimental Results
44(3)
Bibliography
45(2)
Chapter 5 Rotating Machinery Gearbox Fault Diagnosis Based on One-Dimensional Convolutional Neural Network and Random Forest
47(12)
5.1 Introduction
47(1)
5.2 Model Establishment and Theoretical Derivation
48(6)
5.2.1 One-Dimensional Convolutional Neural Network
49(1)
5.2.2 Random Forest Algorithm
50(1)
5.2.3 The Proposed Fault Diagnosis Model
51(1)
5.2.4 Error Back Propagation of the Proposed Model
51(3)
5.2.5 Weights Optimization Using Adaptive Moments
54(1)
5.3 Experimental Results
54(5)
5.3.1 Experimental Platform
54(1)
5.3.2 Experimental Setup
55(1)
5.3.3 Analysis of Experimental Results
55(2)
Bibliography
57(2)
Chapter 6 Fault Diagnosis for Rotating Machinery Gearbox Based on Improved Random Forest Algorithm
59(8)
6.1 Introduction
59(1)
6.2 Improved Random Forest Algorithm
60(3)
6.2.1 Semi-Supervised Learning
60(2)
6.2.2 Improved Random Forest Classification Algorithm
62(1)
6.3 Experimental Verification
63(4)
Bibliography
64(3)
Chapter 7 Imbalanced Data Fault Diagnosis Based on Hybrid Feature Dimensionality Reduction and Varied Density-Based Safe-Level Synthetic Minority Oversampling Technique
67(12)
7.1 Introduction
67(1)
7.2 Design of Hybrid Feature Dimensionality Reduction Algorithm
68(3)
7.2.1 Sensitive Feature Selection
69(1)
7.2.2 Dimension Reduction of Features
70(1)
7.3 Design of Varied Density-Based Safe-Level Synthetic Minority Oversampling Technique
71(1)
7.4 Experiment and Results
72(7)
7.4.1 Data Classification Method
72(2)
7.4.2 Experiment Platform
74(1)
7.4.3 Feature Extraction
74(1)
7.4.4 Data Acquisition
75(1)
7.4.5 Results Analysis
76(1)
Bibliography
77(2)
Index 79
Rui Yang received the B.Eng. degree in Computer Engineering and the Ph.D. degree in Electrical and Computer Engineering from National University of Singapore in 2008 and 2013 respectively. He is currently an Assistant Professor in the School of Advanced Technology, Xian Jiaotong-Liverpool University, Suzhou, China, and an Honorary Lecturer in the Department of Computer Science, University of Liverpool, Liverpool, United Kingdom. His research interests include machine learning based data analysis and applications.

Maiying Zhong received her Ph.D. degree in control theory and control engineering from the Northestern University, China, in 1999. From 2000 to 2001, she was a visiting Scholar at the University of Applied Sciences Lausitz, Germany. From 2002 to July 2008, she was a professor of the School of Control Science and Engineering at Shandong University. From 2006 to 2007, she was a Postdoctoral Researcher Fellow with the Central Queensland University, Australia. From 2009 to 2016, she was a professor of the School of Instrument Science and Opto-Electronics Engineering, Beihang University. In March 2016, she joined Shandong University of Science and Technology, China, where she is currently a professor with the College of Electrical Engineering and Automation. Her research interests are model based fault diagnosis, fault tolerant systems and their applications.
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