{"title":"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","authors":"Zhen Wang;Guangjie Han;Li Liu;Yuanyang Zhu;Yilixiati Abudurexiti","doi":"10.1109/TIM.2025.3606044","DOIUrl":null,"url":null,"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.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Shapley Value-Based Method for Formulating Physical Mechanism Semantics of Signal Sequences in Interpretable Fault Diagnosis\",\"authors\":\"Zhen Wang;Guangjie Han;Li Liu;Yuanyang Zhu;Yilixiati Abudurexiti\",\"doi\":\"10.1109/TIM.2025.3606044\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-16\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11151253/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11151253/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Shapley Value-Based Method for Formulating Physical Mechanism Semantics of Signal Sequences in Interpretable Fault Diagnosis
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.
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.