Knowledge Commons of Institute of Automation,CAS
Uncertainty quantification of bearing remaining useful life based on convolutional neural network | |
Wang Huanjie1,2![]() ![]() ![]() | |
2020-12 | |
会议名称 | 2020 IEEE Symposium Series on Computational Intelligence |
会议日期 | 2020-12 |
会议地点 | Canberra, Australia |
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. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 人工智能+制造 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51835 |
专题 | 中科院工业视觉智能装备工程实验室_工业智能技术与系统 |
通讯作者 | Tan, Jie |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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],2020. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Uncertainty_Quantifi(952KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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