Intelligent Prognostics for Engineering Systems with Machine Learning Techniques
eBook - ePub

Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

Gunjan Soni, Om Prakash Yadav, Gaurav Kumar Badhotiya, Mangey Ram, Gunjan Soni, Om Prakash Yadav, Gaurav Kumar Badhotiya, Mangey Ram

  1. 244 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

Gunjan Soni, Om Prakash Yadav, Gaurav Kumar Badhotiya, Mangey Ram, Gunjan Soni, Om Prakash Yadav, Gaurav Kumar Badhotiya, Mangey Ram

Book details
Table of contents
Citations

About This Book

The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.

The book

  • Discusses basic as well as advance research in the field of prognostics
  • Explores integration of data collection, fault detection, degradation modeling and reliability prediction in one volume
  • Covers prognostics and health management (PHM) of engineering systems
  • Discusses latest approaches in the field of prognostics based on machine learning

The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

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Information

Publisher
CRC Press
Year
2023
ISBN
9781000954104
Edition
1
Topic
Tecnología e ingeniería
Subtopic
Ingeniería industrial

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. List of contributors
  10. Chapter 1 A bibliometric analysis of research on tool condition monitoring
  11. Chapter 2 Predicting restoration factor for different maintenance types
  12. Chapter 3 Measurement and modeling of cutting tool temperature during dry turning operation of DSS
  13. Chapter 4 Leaf disease recognition: Comparative analysis of various convolutional neural network algorithms
  14. Chapter 5 On the validity of parallel plate assumption for modelling leakage flow past hydraulic piston-cylinder configurations
  15. Chapter 6 Development of a hybrid MGWO-optimized support vector machine approach for tool wear estimation
  16. Chapter 7 The energy consumption optimization using machine learning technique in electrical arc furnaces (EAF)
  17. Chapter 8 PID-based ANN control of dynamic systems
  18. Chapter 9 Fatigue damage prognosis of offshore piping
  19. Chapter 10 Minimization of joint angle jerk for industrial manipulator based on prognostic behaviour
  20. Chapter 11 Estimation of bearing remaining useful life using exponential degradation model and random forest algorithm
  21. Chapter 12 Machine learning-based predictive maintenance for diagnostics and prognostics of engineering systems
  22. Index