Knowledge Commons of Institute of Automation,CAS
Uncertainty Quantification in Remaining Useful Life Prediction under Covariate Shift | |
Wang Huanjie1,2; Bai Xiwei1,2; Tan Jie1,2; Liu Chengbao1,2 | |
发表期刊 | Neural Computing and Applications |
2023 | |
页码 | 28 |
摘要 | Data-driven prognostic methods centered on deep learning are attracting ever-increasing attention. However, most existing methods mainly provide point estimates about RUL without quantifying predictive uncertainty, making it difficult to determine the confidence of the prediction results. Additionally, the uncertainty of the degradation process can cause distribution discrepancies between different machines, even under the same working conditions. While most probabilistic deep learning methods can provide reliable predictive uncertainty over in-distribution data, these methods tend to output overconfident predictions under the covariate shift situation where data distribution changes across domains while the conditional distribution stays the same. To solve the above two issues, this paper proposes a Bayesian framework-based method that is easy to implement and can provide high-quality predictive uncertainty under covariate shift. The proposed method provides a framework to unify uncertainty quantification and distribution alignment by probabilistic modeling. First, this method models both model and data uncertainty to make more reliable maintenance decisions. The model uncertainty is handled by variational inference that approximates the posterior distribution over the model parameters. The data uncertainty is captured by Gaussian distribution parameterized. Then we adopt domain adaptation to reduce the distribution discrepancy of different domains, which are extracted by the Bayesian framework-based model. The calibration of uncertainty estimates can provide a more appropriate quantification of uncertainty under covariate shift. The proposed method is validated using vibration signals obtained from the accelerated degradation of rolling element bearings. The results of the experiments show the effectiveness of RUL prediction of machinery. |
关键词 | Remaining useful life Uncertainty quantification Bayesian convolutional neural network |
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 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52053 |
专题 | 中国科学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,et al. Uncertainty Quantification in Remaining Useful Life Prediction under Covariate Shift[J]. Neural Computing and Applications,2023:28. |
APA | Wang Huanjie,Bai Xiwei,Tan Jie,&Liu Chengbao.(2023).Uncertainty Quantification in Remaining Useful Life Prediction under Covariate Shift.Neural Computing and Applications,28. |
MLA | Wang Huanjie,et al."Uncertainty Quantification in Remaining Useful Life Prediction under Covariate Shift".Neural Computing and Applications (2023):28. |
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