TY - JOUR
T1 - Vibration signal-based early fault prognosis: Status quo and applications
AU - Lv, Yaqiong
AU - Zhao, Wenqin
AU - Zhao, Zhiyao
AU - Li, Weidong
AU - Ng, Kam K.H.
N1 - Funding Information:
This research was sponsored by the Humanities and Social Science Foundation of Ministry of Education of China (Project No. 20YJC630096 ) as well as supported by the Open Research Fund Program of Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University and partially sponsored by the National Natural Science Foundation of China (Project No. 72101194 , 51975444 & 61903008 ).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/4
Y1 - 2022/4
N2 - To implement Prognostics and Health Management (PHM) for industrial systems, it is paramount to conduct early fault prognosis on the systems to ensure the stability and reliability during their entire lifecycles. Investigations on early fault prognosis have been actively carried out, but there is a lack of systematic analysis and summary of the developed methods. To bridge the gap, in this paper, the relevant methods are comprehensively reviewed from the aspects of signal processing and fault identification. Furthermore, the applications of the methods are systematically described. In the end, to further facilitate researchers and practitioners, statistical and comparative analysis of the reviewed methods are given, and future development directions are outlined.
AB - To implement Prognostics and Health Management (PHM) for industrial systems, it is paramount to conduct early fault prognosis on the systems to ensure the stability and reliability during their entire lifecycles. Investigations on early fault prognosis have been actively carried out, but there is a lack of systematic analysis and summary of the developed methods. To bridge the gap, in this paper, the relevant methods are comprehensively reviewed from the aspects of signal processing and fault identification. Furthermore, the applications of the methods are systematically described. In the end, to further facilitate researchers and practitioners, statistical and comparative analysis of the reviewed methods are given, and future development directions are outlined.
KW - Deep learning
KW - Early fault prognosis
KW - Signal processing
KW - Vibration signal analysis
UR - http://www.scopus.com/inward/record.url?scp=85127771519&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2022.101609
DO - 10.1016/j.aei.2022.101609
M3 - Review article
AN - SCOPUS:85127771519
SN - 1474-0346
VL - 52
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 101609
ER -