A source free robust domain adaptation approach with pseudo-labels uncertainty estimation for rolling bearing fault diagnosis under limited sample conditions

域适应 方位(导航) 断层(地质) 适应(眼睛) 样品(材料) 估计 领域(数学分析) 计算机科学 模式识别(心理学) 可靠性工程 数据挖掘 人工智能 工程类 数学 地质学 地震学 心理学 系统工程 分类器(UML) 数学分析 化学 色谱法 神经科学
作者
Ruiqi Liu,Wengang Ma,Feipeng Kuang,Guo Jin,Ning Zhao
出处
期刊:Knowledge Based Systems [Elsevier BV]
卷期号:304: 112443-112443 被引量:1
标识
DOI:10.1016/j.knosys.2024.112443
摘要

As essential components of machinery equipment, rolling bearings directly affect the safety of the machinery equipment. The timely diagnosis of bearing faults can effectively prevent equipment lapses. However, bearings are often inconsistently distributed. This has resulted in a significant decrease in their availability. Moreover, the performances of traditional models are poor when fault samples are scarce. The unsupervised domain adaptation (UDA) model based on the transfer learning theory can solve the above problems in static scenarios. However, source domain data are often not directly accessible for privacy protection. Therefore, achieving the robustness of UDA models is significantly challenging. Source-free UDA can achieve a positive transfer from the source domain to the target domain based only on a pretrained source-domain model and unlabeled target-domain data. In this study, we built a source-free robust UDA approach with pseudo-label uncertainty estimation (SFRDA-PLUE) for diagnosing bearing faults using a limited number of samples. First, we designed a robust feature extractor (SANet) and proposed a novel binary soft-constrained information entropy. This was applied to solve the problem that standard information entropy cannot effectively estimate the uncertainty of pseudo-labels. In addition, we constructed a weighted comparison filter strategy to smoothen the fuzzy samples. Finally, we introduced an information-maximizing loss strategy to optimize the performance of the source domain classifier and the pseudo-label estimator. Thus, the robustness of the pseudo-label uncertainty estimation was significantly improved. The experimental results validated that the SFRDA-PLUE approach can achieve excellent diagnostic performance under a limited number of samples.
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