使用加权伪标签的标签引导对比学习:注释数据不足情况下的新型机械故障诊断方法

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Xinyu Li , Changming Cheng , Zhike Peng
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引用次数: 0

摘要

探索对标注数据依赖性较弱的机械设备故障诊断方法对工业生产至关重要。对比学习(CL)能够在没有标注信息的情况下学习表征,在机械故障诊断方面取得了令人满意的成绩。然而,目前基于对比学习的方法主要有两个局限性。首先,预训练阶段只使用未标注或已标注的样本,而微调阶段只依赖已标注的样本,导致样本利用效率低下。其次,仅在借口任务中通过对比损失学习到的表示对于下游诊断任务来说是次优的。针对这些问题,本文提出了一种基于标签引导对比学习(LgCL)和加权伪标记(WPL)策略的新型诊断框架,以提高故障诊断的准确性。在预训练阶段,所提出的 LgCL 将两种类型的对比损失与分类损失整合在一起,使编码器能够学习直接有利于下游诊断任务的判别表征。所设计的混合微调策略允许标注和未标注数据通过伪标注参与微调,从而增强了模型的泛化能力。有针对性地设计的 WPL 策略减轻了伪标签的噪声缺陷。在两个公共数据集和一个自行设计的数据集上进行的对比和消融实验验证了所提出的方法在有限标注数据的故障诊断方面的优越性,诊断准确率比监督、半监督和对比学习方法分别提高了 25.30%、5.47% 和 10.02%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Label-guided contrastive learning with weighted pseudo-labeling: A novel mechanical fault diagnosis method with insufficient annotated data
Exploring fault diagnosis methods for mechanical equipment with weak dependency on annotated data is essential for industrial production. Contrastive learning (CL), capable of learning representations without labeling information, has achieved satisfactory performance in mechanical fault diagnosis. However, current CL-based approaches mainly encounter two limitations. First, the pre-training stage uses either unannotated or annotated samples exclusively while the fine-tuning stage solely relies on annotated ones, leading to inefficient sample utilization. Second, the representation learned by contrastive loss alone in the pretext task is sub-optimal for downstream diagnostic tasks. To address these issues, this paper proposed a novel diagnostic framework based on label-guided contrastive learning (LgCL) and weighted pseudo-labeling (WPL) strategy to improve fault diagnosis accuracy. In the pre-training stage, the proposed LgCL integrates two types of contrastive loss together with classification loss, enabling the encoder to learn discriminative representations that directly benefit the downstream diagnostic task. The devised hybrid fine-tuning strategy allows both labeled and unlabeled data to participate in fine-tuning via pseudo-labeling, enhancing model generalization. The pertinently designed WPL strategy mitigates the defect of noisy pseudo labels. Comparison and ablation experiments on two public datasets and one self-designed dataset validate the superiority of the proposed method for fault diagnosis with limited annotated data, with diagnostic accuracies improved by 25.30%, 5.47% and 10.02% over supervised, semi-supervised and contrastive learning methods, respectively.
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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