Yi Ruan;Xinlong Yu;Yigang He;Yuanjie Fang;Jing Wang;Hao Cong
{"title":"Hierarchical Fault Diagnosis Method for Piezoresistive Pressure Sensor Based on GAF and CNN-SVM","authors":"Yi Ruan;Xinlong Yu;Yigang He;Yuanjie Fang;Jing Wang;Hao Cong","doi":"10.1109/TIM.2025.3582310","DOIUrl":null,"url":null,"abstract":"The requirements for reliability of piezoresistive pressure sensor in modern society are getting higher and higher because of wide application range of sensor and complex working environments. However, due to material properties and working principle (Wheatstone bridge), the failure caused by the degradation of different internal components in piezoresistive pressure sensors exhibits high degree of approximation, mainly reflected in the change in sensor output sensitivity coefficient and signal nonlinear distortion. This brings some challenges to the fault diagnosis of piezoresistive pressure sensor, including how to effectively extract features that can characterize different faults, how to accurately identify faults, and how to judge the severity of corresponding faults. To solve the above crucial problems, a hierarchical fault diagnosis (HFD) method based on Gramian angular fields (GAFs) and convolutional neural networks constructed with support vector machine (CNN-SVM) is presented in this article. First, the piezoresistive pressure sensor fault types are defined according to the functional area. Second, GAF is adopted to encode original output signal of the sensor, which can solve the problem of weak fault features in the original signal. Third, CNN is used to extract the features of Gramian angular summation field (GASF) images transformed by GAF. Moreover, SVM is used as the classifier connected to the flattening layer of CNN. Final, an HFD architecture is proposed to realize fault identification and fault severity judgment. The experimental results indicate the method in this article can effectively extract features, accurately identify different faults of the pressure sensor, and judge the severity of the fault. The accuracy of fault identification achieves 99.34% and the highest accuracy of severity judgment achieves 98%. It proves that the method proposed in this article is applicable and efficient for industrial application.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6000,"publicationDate":"2025-06-23","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/11048665/","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}
引用次数: 0
Abstract
The requirements for reliability of piezoresistive pressure sensor in modern society are getting higher and higher because of wide application range of sensor and complex working environments. However, due to material properties and working principle (Wheatstone bridge), the failure caused by the degradation of different internal components in piezoresistive pressure sensors exhibits high degree of approximation, mainly reflected in the change in sensor output sensitivity coefficient and signal nonlinear distortion. This brings some challenges to the fault diagnosis of piezoresistive pressure sensor, including how to effectively extract features that can characterize different faults, how to accurately identify faults, and how to judge the severity of corresponding faults. To solve the above crucial problems, a hierarchical fault diagnosis (HFD) method based on Gramian angular fields (GAFs) and convolutional neural networks constructed with support vector machine (CNN-SVM) is presented in this article. First, the piezoresistive pressure sensor fault types are defined according to the functional area. Second, GAF is adopted to encode original output signal of the sensor, which can solve the problem of weak fault features in the original signal. Third, CNN is used to extract the features of Gramian angular summation field (GASF) images transformed by GAF. Moreover, SVM is used as the classifier connected to the flattening layer of CNN. Final, an HFD architecture is proposed to realize fault identification and fault severity judgment. The experimental results indicate the method in this article can effectively extract features, accurately identify different faults of the pressure sensor, and judge the severity of the fault. The accuracy of fault identification achieves 99.34% and the highest accuracy of severity judgment achieves 98%. It proves that the method proposed in this article is applicable and efficient for industrial application.
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.