Zare Samira, Ayati Moosa
School of Mechanical Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran.
School of Mechanical Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran.
ISA Trans. 2021 Feb;108:230-239. doi: 10.1016/j.isatra.2020.08.021. Epub 2020 Aug 21.
Wind turbine technology is pursuing the maturation using advanced multi-megawatt machinery equipped by powerful monitoring systems. In this work, a multichannel convolutional neural network is employed to develop an autonomous databased fault diagnosis algorithm. This algorithm has been evaluated in a 5MW wind turbine benchmark model. Several faults for various wind speeds are simulated in the benchmark model, and output data are recorded. A multichannel convolutional neural network with multiple parallel local heads is utilized in order to consider changes in every measured variable separately to identify subsystem faults. Time-domain signals obtained from the wind turbine are portrayed as images and fed independently to the proposed network. Results show that the multivariable fault diagnosis scheme diagnoses the most common wind turbine faults and achieves high accuracy.
风力涡轮机技术正在借助配备强大监测系统的先进多兆瓦机械来实现成熟。在这项工作中，采用了多通道卷积神经网络来开发一种基于数据库的自主故障诊断算法。该算法已在一个5兆瓦风力涡轮机基准模型中进行了评估。在基准模型中模拟了不同风速下的几种故障，并记录了输出数据。使用了具有多个并行局部头的多通道卷积神经网络，以便分别考虑每个测量变量的变化来识别子系统故障。从风力涡轮机获得的时域信号被描绘为图像，并独立输入到所提出的网络中。结果表明，多变量故障诊断方案能够诊断出最常见的风力涡轮机故障，并实现了高精度。
