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Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring
Structural Health Monitoring ( IF 5.7 ) Pub Date : 2021-04-10 , DOI: 10.1177/14759217211004822
Fausto Pedro García Márquez 1 , Pedro José Bernalte Sánchez 1 , Isaac Segovia Ramírez 1
Affiliation  

Wind energy is considered as one of the most important renewable energies in the world, employing larger and more complex wind turbines. They need novel condition monitoring systems to ensure the reliability, availability, safety and maintainability of the main components of the wind turbines. It leads to early fault detection, increasing the productivity and minimizing the maintenance costs and downtimes. This article proposes a novel non-destructive testing system to analyse acoustically rotatory devices of wind turbines. It captures the noise emitted by the devices using an acoustic condition monitoring system embedded in an unmanned aerial vehicle. The signal acquired is sent to ground computer station for recording and analysing the data. It uses a test rig, previously validated, to carry out a set of experiments to simulate the main faults. A signal processing method is done by wavelet transforms that filters and analyses the energy patterns of the signals. The results are analysed qualitatively and quantitatively considering different scenarios. A statistical analysis is developed to compare the numerical results provided by different wavelet transform families and convolutional neural network. It is concluded that Symlets and Daubechies families report equivalent results for this case study. The accuracies of the results are more than 75%, reaching up to 100%. The approach is validated employing Friedman test.



中文翻译:

用于风力涡轮机结构健康监测的无人飞行器声学检查系统

风能被认为是世界上最重要的可再生能源之一,它采用了更大,更复杂的风力涡轮机。他们需要新颖的状态监控系统,以确保风力涡轮机主要组件的可靠性,可用性,安全性和可维护性。它可以及早发现故障,提高生产率并最大程度地减少维护成本和停机时间。本文提出了一种新颖的非破坏性测试系统来分析风力涡轮机的声学旋转装置。它使用嵌入在无人飞行器中的声学状态监测系统捕获设备发出的噪声。所获取的信号被发送到地面计算机站,以记录和分析数据。它使用事先经过验证的测试设备进行一组实验,以模拟主要故障。通过小波变换来完成信号处理方法,该小波变换对信号的能量模式进行滤波和分析。考虑到不同的情况,对结果进行定性和定量分析。进行统计分析以比较不同小波变换族和卷积神经网络提供的数值结果。结论是,Symlets和Daubechies家庭报告了此案例研究的等效结果。结果的准确性超过75%,最高可达100%。该方法已通过弗里德曼检验进行了验证。进行统计分析以比较不同小波变换族和卷积神经网络提供的数值结果。结论是,Symlets和Daubechies家庭报告了此案例研究的等效结果。结果的准确性超过75%,最高可达100%。该方法通过弗里德曼检验进行了验证。进行统计分析以比较不同小波变换族和卷积神经网络提供的数值结果。结论是,Symlets和Daubechies家庭报告了此案例研究的等效结果。结果的准确性超过75%,最高可达100%。该方法已通过弗里德曼检验进行了验证。

更新日期:2021-04-11
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