A Shapley Value-Based Method for Formulating Physical Mechanism Semantics of Signal Sequences in Interpretable Fault Diagnosis

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Zhen Wang;Guangjie Han;Li Liu;Yuanyang Zhu;Yilixiati Abudurexiti
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Abstract

Despite significant advancements in deep learning (DL) for fault diagnosis, the black-box nature of DL models hinders their reliable deployment in industrial applications. Interpretability methods have emerged to address this opacity, yet their effectiveness remains limited due to the lack of unified semantic guidance. This semantic gap not only constrains their practical application but also creates a disconnect between post hoc model explanations and ante hoc model guidance. In addition, the absence of quantitative metrics makes it challenging to evaluate the trustworthiness of interpretability methods. To address these challenges, this article proposes a signal semantic evaluation strategy (SSES), establishing a unified semantic framework. SSES employs Shapley values to evaluate significant signal components in the frequency domain. By integrating physical mechanisms, SSES enhances evaluation accuracy and formulates interpretable fault semantics. Furthermore, adversarial training and model ensemble strategies are employed to enhance the evaluation stability. To assess the reliability of interpretability methods, we introduce two metrics that quantify the consistency between constructed semantics and actual semantics. Experiments on two public datasets demonstrate that SSES accurately identifies significant signal components, while the proposed metrics effectively quantify interpretation reliability. Experiments on the XJTU-SY and Case Western Reserve University (CWRU) datasets demonstrate that SSES accurately identifies significant signal components and achieves the highest diagnostic accuracy under noise interference, reaching 86.7% and 99.1% at 0 dB noise level, respectively. In addition, the proposed reliability metrics effectively quantify interpretation reliability, showing that models with higher reliability scores exhibit superior robustness to noise.
基于Shapley值的可解释故障诊断信号序列物理机制语义表达方法
尽管深度学习(DL)在故障诊断方面取得了重大进展,但DL模型的黑箱性质阻碍了它们在工业应用中的可靠部署。可解释性方法已经出现以解决这种不透明性,但由于缺乏统一的语义指导,它们的有效性仍然有限。这种语义差距不仅限制了它们的实际应用,而且还造成了事后模型解释和事前模型指导之间的脱节。此外,定量指标的缺乏使得评估可解释性方法的可信度具有挑战性。针对这些挑战,本文提出了一种信号语义评价策略(ses),建立统一的语义框架。ses采用Shapley值来评估频域中的重要信号分量。通过集成物理机制,提高了故障评估的准确性,并形成了可解释的故障语义。此外,采用对抗训练和模型集成策略来提高评估的稳定性。为了评估可解释性方法的可靠性,我们引入了两个量化构建语义与实际语义之间一致性的指标。在两个公共数据集上的实验表明,该方法能够准确地识别重要信号成分,而所提出的指标能够有效地量化解释可靠性。在西安交大- sy和凯斯西储大学(CWRU)数据集上的实验表明,该方法能够准确识别重要信号成分,在噪声干扰下的诊断准确率最高,在0 dB噪声水平下分别达到86.7%和99.1%。此外,所提出的可靠性指标有效地量化了解释的可靠性,表明可靠性得分较高的模型对噪声具有更好的鲁棒性。
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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