This book presents systematic overviews and bright insights into big
data-driven intelligent fault diagnosis and prognosis for mechanical
systems. The recent research results on deep transfer learning-based
fault diagnosis, data-model fusion remaining useful life (RUL)
prediction, etc., are focused on in the book. The contents are valuable
and interesting to attract academic researchers, practitioners, and
students in the field of prognostics and health management (PHM).
Essential guidelines are provided for readers to understand, explore,
and implement the presented methodologies, which promote further
development of PHM in the big data era.
Features:
- Addresses the critical challenges in the field of PHM at present
- Presents both fundamental and cutting-edge research theories on
intelligent fault diagnosis and prognosis
- Provides abundant experimental validations and engineering cases of
the presented methodologies