Cross-domain decision method based on instance transfer and model transfer for fault diagnosis

IF 2.1 4区 工程技术
Jiaqing Zhang, Yubiao Huang, Rui Liu, Zijian Wu
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引用次数: 0

Abstract

As the digitalization of industrial assets advances, data-driven fault diagnosis has increasingly garnered attention. However, models often underperform due to the lack of sufficient training data and the complexity of operational environments. In scenarios where a similar task with abundant data exists in the source domain, leveraging the knowledge embedded in this source data could be key to constructing an effective diagnostic model for the target domain. Following this idea, this study introduces a novel cross-domain decision method, weighted structure expansion and reduction (WSER), for fault diagnosis. This method initially extracts features from the time, frequency, and time-frequency domains. It then estimates data weights following the idea of instance transfer to mitigate the dissimilarity between the source and target data distributions. Based on these estimated weights, feature selection is further performed. The extracted source knowledge is subsequently transferred to the target domain using the proposed WSER method. The proposed method is applied on two public engineering fault datasets, and the results demonstrate the effectiveness of the proposed method in increasing the accuracy of fault diagnosis.
基于实例转移和模型转移的故障诊断跨域决策方法
随着工业资产数字化的推进,数据驱动的故障诊断越来越受到关注。然而,由于缺乏足够的训练数据和运行环境的复杂性,模型往往表现不佳。在源领域存在大量数据的类似任务的情况下,利用这些源数据中蕴含的知识可能是为目标领域构建有效诊断模型的关键。根据这一想法,本研究为故障诊断引入了一种新型跨领域决策方法--加权结构扩展和缩减(WSER)。该方法首先从时域、频域和时频域提取特征。然后,它按照实例转移的思想估算数据权重,以减轻源数据和目标数据分布之间的差异。在这些估计权重的基础上,进一步进行特征选择。随后,提取的源知识将通过拟议的 WSER 方法转移到目标领域。建议的方法被应用于两个公共工程故障数据集,结果表明建议的方法在提高故障诊断的准确性方面非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Mechanical Engineering
Advances in Mechanical Engineering Engineering-Mechanical Engineering
自引率
4.80%
发文量
353
期刊介绍: Advances in Mechanical Engineering (AIME) is a JCR Ranked, peer-reviewed, open access journal which publishes a wide range of original research and review articles. The journal Editorial Board welcomes manuscripts in both fundamental and applied research areas, and encourages submissions which contribute novel and innovative insights to the field of mechanical engineering
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