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| Research on Bearing Condition Monitoring based on deep learning |
| Liang Guo, Hongli Gao, Yiwen Zhang, Haifeng Huang |
| Southwest jiaotong university, college of mechanical engineering, chengdu, 610031 |
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Abstract Vibration signal of rolling element bearing is in a high degree of coupling, so that the features and recognition model are difficult to build. For solving those problems, we proposed a novel bearing condition monitoring model that is based on deep learning. Time domain, frequency domain and time-frequency domain features are extracted. Then those feature vectors are entered into unsupervised auto-encoder to learning high-level features. At the same, the middle layers of auto-encoder network are stacked into multilayered network. Finally, a small number of labeled training samples are used to fine-turning deep learning network. The bearing condition recognition experiment shows that the proposed method gets the state of the art result, and its high accuracy of performance degradation condition is really helpful for condition-based maintenance.
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Received: 12 March 2015
Published: 15 June 2016
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