Uncertainty quantification of bearing remaining useful life based on convolutional neural network | |
Wang Huanjie1,2; Bai Xiwei1,2; Tan, Jie1,2 | |
2020-12 | |
会议日期 | 2020-12 |
会议地点 | Canberra, Australia |
英文摘要 | Remaining useful life (RUL) prediction is critical for predictive maintenance of machinery. Data-driven prognostics methods centered on deep learning are attracting ever-increasing attention. However, most existing methods mainly provide point estimates about RUL without quantifying predictive uncertainty. In contrast, Bayesian models can offer a reliable framework for estimating predictive uncertainty, but these models require expensive computation cost. In this paper, we present a Bayesian framework based convolutional neural network (BCNN) that is easy to implement and can provide high-quality predictive uncertainty of RUL. The variational inference is adopted to approximate the posterior distribution over the model parameters. Then the approximating probability distribution is used for subsequent inference of newly observed data. The proposed method is validated using vibration signals obtained from the accelerated degradation of rolling element bearings. The timefrequency domain features are extracted from raw vibration signals using continuous wavelet transform. The results of the experiments show the effectiveness of the RUL prediction of machinery. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/51835] |
专题 | 综合信息系统研究中心_工业智能技术与系统 |
通讯作者 | Tan, Jie |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Wang Huanjie,Bai Xiwei,Tan, Jie. Uncertainty quantification of bearing remaining useful life based on convolutional neural network[C]. 见:. Canberra, Australia. 2020-12. |
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