@article{1453133,
      recid = {1453133},
      author = {Lei, Yaguo, and Li, Naipeng, and Li, Xiang,},
      title = {Big-data driven intelligent fault diagnosis and prognosis  for mechanical systems /},
      pages = {1 online resource (xiii, 281 pages) :},
      abstract = {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.},
      url = {http://library.usi.edu/record/1453133},
      doi = {https://doi.org/10.1007/978-981-16-9131-7},
}