期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 15, 期 6, 页码 3703-3711出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2868687
关键词
Bearings; deep neural networks (DNNs); prognosis; remaining useful life (RUL) prediction
类别
资金
- Natural Sciences and Engineering Research Council (NSERC) of Canada
- Canada Foundation for Innovation (CFI)
- British Columbia Knowledge Development Fund (BCKDF)
- National Key Research and Development Program of China [2017YFE0101000]
- Science and Technology Program of Guangzhou, China [201802030005]
- Key Program of Natural Science Foundation of Guangdong Province [2017B030311008]
The degradation of bearings plays a key role in the failures of industrial machinery. Prognosis of bearings is critical in adopting an optimal maintenance strategy to reduce the overall cost and to avoid unwanted downtime or even casualties by estimating the remaining useful life (RUL) of the bearings. Traditional data-driven approaches of RUL prediction rely heavily on manual feature extraction and selection using human expertise. This paper presents an innovative two-stage automated approach to estimate the RUL of bearings using deep neural networks (DNNs). A denoising autoencoder-based DNN is used to classify the acquired signals of the monitored bearings into different degradation stages. Representative features are extracted directly from the raw signal by training the DNN. Then, regression models based on shallow neural networks are constructed for each health stage. The final RUL result is obtained by smoothing the regression results from different models. The proposed approach has achieved satisfactory prediction performance for a real bearing degradation dataset with different working conditions.
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