A Novel Spatiotemporal Enhanced Convolutional Autoencoder Network for Unsupervised Health Indicator Construction

The health indicator (HI) of rotating machinery affects the reliability and accuracy of its remaining useful life (RUL) prediction. Convolutional autoencoder (CAE) is widely used for HI construction with the advantage of being unsupervised, but it still suffers from two problems: 1) the shallow lear...

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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 10
Hlavní autoři: Li, Shuaiyong, Zhang, Chao, Zhang, Xuyuntao
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Convolution
Data mining
Feature extraction
Health indicator (HI)
Machinery
Mining
Modules
remaining useful life (RUL)
Roller bearings
Rotating machinery
spatiotemporal enhanced convolutional autoencoder (STEACE)
Time-domain analysis
Time-varying systems
unsupervised learning
ISSN:0018-9456, 1557-9662
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Shrnutí:The health indicator (HI) of rotating machinery affects the reliability and accuracy of its remaining useful life (RUL) prediction. Convolutional autoencoder (CAE) is widely used for HI construction with the advantage of being unsupervised, but it still suffers from two problems: 1) the shallow learning of local spatial features and the neglect of global temporal features result in poor performance of the constructed HI trend and 2) a non-end-to-end form that requires human involvement, which is time-consuming and labor-intensive. Therefore, a novel unsupervised spatiotemporal enhanced convolutional autoencoder (STECAE) network is proposed in this article, which can directly enhance feature mining on raw data in both temporal and spatial dimensions without any prior knowledge. The STECAE network mainly consists of three components: spatial mining module (SMM), temporal learning module (TLM), and trend compensation mechanism (TCM). SMM enhances the deep mining of local spatial features via deep residual shrinkage module. TLM mines global temporal features through the gate recurrent unit (GRU). TCM acts as a bridge for spatiotemporal connectivity to improve the model's global sensing field and HI's trend performance. The effectiveness of the proposed method is validated through the dataset of rolling bearings and aero engines. The results show that compared to the existing state-of-the-art methods, the HI constructed by the proposed method is closer to 1 in terms of the evaluation metric values and has a lower RUL prediction error. The high-quality HI constructed by the proposed method can provide a reliable basis for RUL prediction.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3383052