Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

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.