{"title":"A Development Strategy for Structural Health Monitoring Applications","authors":"P. Cawley","doi":"10.1115/1.4051974","DOIUrl":null,"url":null,"abstract":"\n Permanently installed structural health monitoring (SHM) systems are now a viable alternative to traditional periodic inspection (nondestructive testing (NDT)). However, their industrial use is limited, and this article reviews the steps required in developing practical SHM systems. The transducers used in SHM are fixed in location, whereas in NDT, they are generally scanned. The aim is to reach similar performance with high temporal frequency, low spatial frequency SHM data to that achievable with conventional high spatial frequency, and low temporal frequency NDT inspections. It is shown that this can be done via change tracking algorithms such as the generalized likelihood ratio (GLR), but this depends on the input data being normally distributed, which can only be achieved if signal changes due to variations in the operating conditions are satisfactorily compensated; there has been much recent progress on this topic, and this is reviewed. Since SHM systems can generate large volumes of data, it is essential to convert the data to actionable information, and this step must be addressed in the SHM system design. It is also essential to validate the performance of installed SHM systems, and a methodology analogous to the model-assisted probability of detection (POD) (MAPOD) scheme used in NDT has been proposed. This uses measurements obtained from the SHM system installed on a typical undamaged structure to capture signal changes due to environmental and other effects and to superpose the signal due to damage growth obtained from finite element predictions. There is a substantial research agenda to support the wider adoption of SHM, and this is discussed in this study.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"9 3 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4051974","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 4
Abstract
Permanently installed structural health monitoring (SHM) systems are now a viable alternative to traditional periodic inspection (nondestructive testing (NDT)). However, their industrial use is limited, and this article reviews the steps required in developing practical SHM systems. The transducers used in SHM are fixed in location, whereas in NDT, they are generally scanned. The aim is to reach similar performance with high temporal frequency, low spatial frequency SHM data to that achievable with conventional high spatial frequency, and low temporal frequency NDT inspections. It is shown that this can be done via change tracking algorithms such as the generalized likelihood ratio (GLR), but this depends on the input data being normally distributed, which can only be achieved if signal changes due to variations in the operating conditions are satisfactorily compensated; there has been much recent progress on this topic, and this is reviewed. Since SHM systems can generate large volumes of data, it is essential to convert the data to actionable information, and this step must be addressed in the SHM system design. It is also essential to validate the performance of installed SHM systems, and a methodology analogous to the model-assisted probability of detection (POD) (MAPOD) scheme used in NDT has been proposed. This uses measurements obtained from the SHM system installed on a typical undamaged structure to capture signal changes due to environmental and other effects and to superpose the signal due to damage growth obtained from finite element predictions. There is a substantial research agenda to support the wider adoption of SHM, and this is discussed in this study.