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
Chapter 1 : Background and Related Methods
Chapter 2 : Fault Diagnosis Method Based on Recurrent Convolutional Neural Network
Chapter 3 : Fault Diagnosis of Rotating Machinery Gear Based on Random Forest Algorithm
Chapter 4 : Bearing Fault Diagnosis under Different Working Conditions Based on Generative Adversarial Networks
Chapter 5 : Rotating Machinery Gearbox Fault Diagnosis Based on One-Dimensional Convolutional Neural Network and Random Forest
Chapter 6 : Fault Diagnosis for Rotating Machinery Gearbox Based on Improved Random Forest Algorithm
Chapter 7 : Imbalanced Data Fault Diagnosis Based on Hybrid Feature Dimensionality Reduction and Varied Density Based Safe-Level Synthetic Minority Oversampling Technique
Index
