Journal
MEASUREMENT
Volume 174, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.108973
Keywords
Rolling bearing; Condition monitoring; Convolutional neural network; Deep learning; Batch normalization
Funding
- National Natural Science Foundation of China [51820105007]
- Guangdong Basic and Applied Basic Research Foundation [2019A1515110642]
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A robust construction based on convolutional neural network (CNN) is established for the condition monitoring of rolling bearings, which can effectively extract features and construct health indicators, showing excellent performances in condition monitoring under variable working conditions and sources.
As one of the most important functional components, the running performances of rolling bearings in rotating machines directly affect the reliability and safety of equipments. However, in actual industrial scenarios, it usually exhibits different failure behaviors caused by harsh and variable working conditions. To effectively guarantee the reliability of operation, a robust construction based on convolutional neural network (CNN) is established for the condition monitoring of rolling bearings. Firstly, the proposed normalized CNN model extracts features from one full-life-cycle data, which contains different state information. Then, the trained model is directly employed for the online monitoring of other rolling bearings. The proposed method is designed to automatically apply to different scenarios and construct health indicators. Two famous datasets are adopted to illustrate the effectiveness and robustness of the proposed method, and the results show that it can achieve excellent performances in condition monitoring under variable working conditions and sources.
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