Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 104, Issue -, Pages 799-834Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2017.11.016
Keywords
Machinery prognostics; Data acquisition; Health indicator construction; Health stage division; Remaining useful life prediction
Categories
Funding
- National Natural Science Foundation of China [51475355, 61673311]
- National Program for Support of Top-notch Young Professionals
- Visiting Scholar Foundation of the State Key Laboratory of Traction Power at Southwest Jiaotong University [TPL1703]
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Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes, i.e., data acquisition, health indicator (HI) construction, health stage (HS) division, and RUL prediction. Over recent years, a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process, i.e., RUL prediction. However, there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap, this paper provides a review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction. First, in data acquisition, several prognostic datasets widely used in academic literature are introduced systematically. Then, commonly used HI construction approaches and metrics are discussed. After that, the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards, the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally, the paper provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field. (C) 2017 Elsevier Ltd. All rights reserved.
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