Max Ovenden, Qing Wang, Songling Huang, Wei Zhao, Shen Wang
{"title":"基于激光位移应变测量的风力机叶片对中实时监测","authors":"Max Ovenden, Qing Wang, Songling Huang, Wei Zhao, Shen Wang","doi":"10.1115/1.4043850","DOIUrl":null,"url":null,"abstract":"Wind turbine (WT) blade structural health monitoring (SHM) is important as it allows damage or misalignment to be detected before it causes catastrophic damage such as that caused by the blade striking the tower. Both of these can be very costly and justify the expense of monitoring. This paper aims to deduce whether a SICK DT-50 laser displacement sensor (LDS) installed inside the tower and a half-bridge type II strain gauge bridge installed at the blade root are capable of detecting ice loading, misalignment, and bolt loosening while the WT is running. Blade faults were detected by the virtual instrument, which conducted a z-test at 99% and 98% significance levels for the LDS and at 99.5% and 99% significance levels for the strain gauge. The significance levels chosen correspond to typical Z-values for statistical tests. A higher significance was used for the strain gauge as it used a one-tail test as opposed to a two-tail test for the LDS. The two different tests were used to test for different sensitivities of the tests. The results show that the strain gauge was capable of detecting all the mass loading cases to 99.5% significance, and the LDS was capable of detecting misalignment, bolt loosening, and 3 out of 4 mass loading cases to 99% significance. It was able to detect the least severe mass loading case of 11 g at the root to only a 98% significance.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"5 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Real-Time Monitoring of Wind Turbine Blade Alignment Using Laser Displacement and Strain Measurement\",\"authors\":\"Max Ovenden, Qing Wang, Songling Huang, Wei Zhao, Shen Wang\",\"doi\":\"10.1115/1.4043850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wind turbine (WT) blade structural health monitoring (SHM) is important as it allows damage or misalignment to be detected before it causes catastrophic damage such as that caused by the blade striking the tower. Both of these can be very costly and justify the expense of monitoring. This paper aims to deduce whether a SICK DT-50 laser displacement sensor (LDS) installed inside the tower and a half-bridge type II strain gauge bridge installed at the blade root are capable of detecting ice loading, misalignment, and bolt loosening while the WT is running. Blade faults were detected by the virtual instrument, which conducted a z-test at 99% and 98% significance levels for the LDS and at 99.5% and 99% significance levels for the strain gauge. The significance levels chosen correspond to typical Z-values for statistical tests. A higher significance was used for the strain gauge as it used a one-tail test as opposed to a two-tail test for the LDS. The two different tests were used to test for different sensitivities of the tests. The results show that the strain gauge was capable of detecting all the mass loading cases to 99.5% significance, and the LDS was capable of detecting misalignment, bolt loosening, and 3 out of 4 mass loading cases to 99% significance. It was able to detect the least severe mass loading case of 11 g at the root to only a 98% significance.\",\"PeriodicalId\":52294,\"journal\":{\"name\":\"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems\",\"volume\":\"5 1\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2019-08-01\",\"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/10.1115/1.4043850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4043850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Real-Time Monitoring of Wind Turbine Blade Alignment Using Laser Displacement and Strain Measurement
Wind turbine (WT) blade structural health monitoring (SHM) is important as it allows damage or misalignment to be detected before it causes catastrophic damage such as that caused by the blade striking the tower. Both of these can be very costly and justify the expense of monitoring. This paper aims to deduce whether a SICK DT-50 laser displacement sensor (LDS) installed inside the tower and a half-bridge type II strain gauge bridge installed at the blade root are capable of detecting ice loading, misalignment, and bolt loosening while the WT is running. Blade faults were detected by the virtual instrument, which conducted a z-test at 99% and 98% significance levels for the LDS and at 99.5% and 99% significance levels for the strain gauge. The significance levels chosen correspond to typical Z-values for statistical tests. A higher significance was used for the strain gauge as it used a one-tail test as opposed to a two-tail test for the LDS. The two different tests were used to test for different sensitivities of the tests. The results show that the strain gauge was capable of detecting all the mass loading cases to 99.5% significance, and the LDS was capable of detecting misalignment, bolt loosening, and 3 out of 4 mass loading cases to 99% significance. It was able to detect the least severe mass loading case of 11 g at the root to only a 98% significance.