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    Real-Time Monitoring of Wind Turbine Blade Alignment Using Laser Displacement and Strain Measurement

    Source: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2019:;volume ( 002 ):;issue: 003::page 31001
    Author:
    Ovenden, Max
    ,
    Wang, Qing
    ,
    Huang, Songling
    ,
    Zhao, Wei
    ,
    Wang, Shen
    DOI: 10.1115/1.4043850
    Publisher: American Society of Mechanical Engineers (ASME)
    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.
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      Real-Time Monitoring of Wind Turbine Blade Alignment Using Laser Displacement and Strain Measurement

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    contributor authorOvenden, Max
    contributor authorWang, Qing
    contributor authorHuang, Songling
    contributor authorZhao, Wei
    contributor authorWang, Shen
    date accessioned2019-09-18T09:02:19Z
    date available2019-09-18T09:02:19Z
    date copyright6/6/2019 0:00
    date issued2019
    identifier issn2572-3901
    identifier othernde_2_3_031001
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4258136
    description abstractWind 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.
    publisherAmerican Society of Mechanical Engineers (ASME)
    titleReal-Time Monitoring of Wind Turbine Blade Alignment Using Laser Displacement and Strain Measurement
    typeJournal Paper
    journal volume2
    journal issue3
    journal titleJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
    identifier doi10.1115/1.4043850
    journal fristpage31001
    journal lastpage031001-13
    treeJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2019:;volume ( 002 ):;issue: 003
    contenttypeFulltext
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