Health indicator construction and status assessment of rotating machinery by spatio-temporal fusion of multi-domain mixed features

•An integrated HI model is constructed by taking advantage of SA, LSTM, and ICAE.•RS is used to exact the multi-domain features of the Fourier transformed signals.•STOA-XGBoost can optimize the parameters of status assessment model automatically.•The validation of SALICAE is verified by both standar...

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Vydáno v:Measurement : journal of the International Measurement Confederation Ročník 205; s. 112170
Hlavní autoři: Duan, Yong, Cao, Xiangang, Zhao, Jiangbin, Xu, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.12.2022
Témata:
Health indicator
Multi-domain mixed features
Rotating machinery
Spatio-temporal fusion
Status assessment
STOA-XGBoost
Multi-domain mixed features
Rotating machinery
Status assessment
STOA-XGBoost
Health indicator
Spatio-temporal fusion
ISSN:0263-2241, 1873-412X
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Shrnutí:•An integrated HI model is constructed by taking advantage of SA, LSTM, and ICAE.•RS is used to exact the multi-domain features of the Fourier transformed signals.•STOA-XGBoost can optimize the parameters of status assessment model automatically.•The validation of SALICAE is verified by both standard database and lab platform. Rotating machinery has been applied in various industries, and weak fault feature monitoring is of great significance to constructing health indicators (HIs) and assessing their status. However, there are some challenges in HI construction and status assessment, including difficult expression of weak features, incomplete information domain, and quantification of early degradation points. To construct a novel HI of rotating machinery, this paper proposes a multi-domain features-based spatio-temporal fusion method, which integrates the spatio-temporal advantages of self-attention (SA), long short-term memory (LSTM), and an improved convolutional autoencoder (ICAE), called SALICAE. On this basis, the sooty tern optimization algorithm (STOA) is used to automatically optimize the extreme gradient boosting model (XGBoost) for assessing the status of rotating machinery accurately. The effectiveness and adaptability of the proposed method are verified by the standard bearing database from Xi’an Jiaotong University, and the average accuracy under different working conditions is approximately 85.3%. Moreover, the accuracy of the proposed method is also tested by the reducer platform organized by our lab, which is 99.3%.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2022.112170