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Bayesian deep-learning for RUL prediction: An active learning perspective

人工智能 计算机科学 机器学习 灵活性(工程) 贝叶斯推理 辍学(神经网络) 推论 人工神经网络 深度学习 贝叶斯概率 数据挖掘 数学 统计
作者
Rong Zhu,Yuan Chen,Weiwen Peng,Zhi‐Sheng Ye
出处
期刊:Reliability Engineering & System Safety [Elsevier BV]
卷期号:228: 108758-108758 被引量:90
标识
DOI:10.1016/j.ress.2022.108758
摘要

Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction in the recent decade. Although with high precision and flexibility, DL methods need sufficient run-to-failure data to guarantee their performance. However, run-to-failure data is fairly expensive to obtain in many industrial applications. How to economically achieve high accuracy with few run-to-failure data becomes a critical and emergent issue. In this study, a Bayesian deep-active-learning framework is proposed for RUL prediction, which goes beyond traditional passive learning and introduces a novel active learning perspective. We use Bayesian neural networks with Monte Carlo dropout inference to predict RUL with uncertainty quantification for samples without run-to-failure labels. The prediction uncertainty is further used to develop an acquisition function for actively selecting target samples to obtain their run-to-failure labels. A recursive model training and active data selection mechanism are then developed to maintain accuracy while reducing the size of the training data. Two practical examples, one from a public bearing dataset and the other from our lab testing on battery degradation, are presented to demonstrate the proposed method. Experimental results demonstrate that 20 and 40% of run-to-failure data can be saved for the bearing and the battery RUL prediction, respectively.
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