
Intelligent Machinery Fault Diagnostics and Prognostics
The Future of Smart Manufacturing
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Intelligent Machinery Fault Diagnostics and Prognostics
The Future of Smart Manufacturing
About this book
The field of machinery maintenance is undergoing a remarkable transformation, driven by the convergence of intelligent technologies and data-driven approaches. This book delves into the fascinating world of intelligent machinery fault diagnostics and prognostics, exploring how these advancements are reshaping the way we monitor, diagnose, and predict faults in machinery.
Intelligent Machinery Fault Diagnostics and Prognostics: The Future of Smart Manufacturing uses an interdisciplinary approach to provide a well-rounded understanding of smart manufacturing. It discusses cutting-edge smart manufacturing technologies and encompasses various aspects, from sensors and data analytics to predictive maintenance. The book offers real-world case studies illustrating how these innovations are successfully implemented in industrial settings and includes practical guidelines and methodologies that facilitate the implementation of solutions.
The book also highlights the scalability and adaptability of this approach to different industries and manufacturing environments. Whether this book is for industry professionals, students, or researchers, readers can leverage the book's insights to optimize machinery performance, minimize downtime, reduce costs, and improve safety in their respective industries.
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Information
Table of contents
- Cover Page
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Contents
- Preface
- About the Editors
- Contributors
- Chapter 1 Introduction to Fault Diagnostics and Prognostics: Direction toward Smart Manufacturing
- Chapter 2 Advanced Diagnostics and Prognostics of Gearbox Faults in Smart Manufacturing: The Critical Role of Gearboxes
- Chapter 3 Vibration and Support Vector Machine-Based Fault Diagnosis of Bevel Gearbox
- Chapter 4 Identifying Inner Race Fault of a Bearing Using Nonlinear Mode Decomposition Technique Supported by Blind Source Separation Methods
- Chapter 5 Detection and Classification of Low-Severity Stator Inter-Turn Faults in Induction Motors Using Temporal Features: A Comparative Machine Learning Approach
- Chapter 6 Feature Selection for Accurate Remaining Useful Life Prediction of Bearing Using Machine Learning
- Chapter 7 Deep Learning and Statistical Model-Based Data-Driven Intelligent Fault Prognostics of Rotary Machinery
- Chapter 8 Remaining Useful Life Prediction for Aircraft Structures Toward Digital Twin Ecosystem
- Chapter 9 Free Vibration Control of Crack Curved Cracked Simple Supported Beams Using Fuzzy Logic Control with Particle Swarm Optimization Tuning
- Chapter 10 Fault Diagnosis of Composite Mono Leaf Spring Based on Vibration Characteristics
- Chapter 11 Current Sensor Fault-Tolerant Control for Model Predictive Control of Induction Motor Drives
- Index