RESEARCH PAPER
Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning
| 1 | School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China |
| 2 | Key Laboratory of Vibration and Control of Aero-Propulsion Systems of Ministry of Education, Northeastern University, Shenyang 110819, China |
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):685–694
HIGHLIGHTS
- Spatial pyramid pooling extracts multi-scale degradation features of bearings.
- TL solves the inconsistent distribution of degraded data for different failed bearings.
- The SPP-CNNTL model shows a better prediction effect on the RUL of the bearing.
KEYWORDS
rolling bearingsRemaining useful life (RUL)Convolutional Neural Networks (CNN)Transfer learning (TL)
ABSTRACT
The accurate prediction of the remaining useful life (RUL) of rolling bearings is of immense importance in ensuring the safe and smooth operation of machinery and equipment. Although the prediction accuracy has been improved by a predictive model based on deep learning, it is still limited in engineering because lots of models use single-scale features to predict and assume that the degradation data of each bearing has a consistent distribution. In this paper, A deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) is proposed to obtain higher prediction accuracy with self-extraction of multi-feature from the original vibrating signal. And to consider the differences of the data distribution in different failure types, transfer learning (TL) added with maximum mean difference (MMD) measurement function is used in the RUL prediction part. Finally, the data of IEEE PHM 2012 Challenge is used for verification, and the results show that the method in this paper has high prediction accuracy
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