Intelligent Machinery Fault Diagnostics and Prognostics
eBook - ePub

Intelligent Machinery Fault Diagnostics and Prognostics

The Future of Smart Manufacturing

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

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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Yes, you can access Intelligent Machinery Fault Diagnostics and Prognostics by Deepam Goyal,Ankit Sharma,Mohamad Abou Houran in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Industrial Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. About the Editors
  9. Contributors
  10. Chapter 1 Introduction to Fault Diagnostics and Prognostics: Direction toward Smart Manufacturing
  11. Chapter 2 Advanced Diagnostics and Prognostics of Gearbox Faults in Smart Manufacturing: The Critical Role of Gearboxes
  12. Chapter 3 Vibration and Support Vector Machine-Based Fault Diagnosis of Bevel Gearbox
  13. Chapter 4 Identifying Inner Race Fault of a Bearing Using Nonlinear Mode Decomposition Technique Supported by Blind Source Separation Methods
  14. Chapter 5 Detection and Classification of Low-Severity Stator Inter-Turn Faults in Induction Motors Using Temporal Features: A Comparative Machine Learning Approach
  15. Chapter 6 Feature Selection for Accurate Remaining Useful Life Prediction of Bearing Using Machine Learning
  16. Chapter 7 Deep Learning and Statistical Model-Based Data-Driven Intelligent Fault Prognostics of Rotary Machinery
  17. Chapter 8 Remaining Useful Life Prediction for Aircraft Structures Toward Digital Twin Ecosystem
  18. Chapter 9 Free Vibration Control of Crack Curved Cracked Simple Supported Beams Using Fuzzy Logic Control with Particle Swarm Optimization Tuning
  19. Chapter 10 Fault Diagnosis of Composite Mono Leaf Spring Based on Vibration Characteristics
  20. Chapter 11 Current Sensor Fault-Tolerant Control for Model Predictive Control of Induction Motor Drives
  21. Index