计算机科学
断层(地质)
滤波器(信号处理)
噪音(视频)
涡轮机
卷积神经网络
控制理论(社会学)
卷积(计算机科学)
干扰(通信)
特征提取
人工智能
模式识别(心理学)
人工神经网络
计算机视觉
工程类
电信
机械工程
频道(广播)
控制(管理)
地震学
图像(数学)
地质学
作者
Fan Yang,Danfeng Huang,Dongdong Li,Yao Zhao,Shunfu Lin,Muyeen S.M.
标识
DOI:10.1088/1361-6501/ac991f
摘要
Abstract The fault diagnosis of a wind turbine gearbox is helpful for reducing the operating costs and risks of wind power systems. However, existing machine-learning-based gearbox fault diagnosis methods have two shortcomings: (a) data samples of gearbox faults are always scarce; and (b) due to the complex structure of gearboxes, the collected vibration signals often contain a large amount of low-frequency noise, which is detrimental to both feature extraction and fault diagnosis. To solve the above two problems, a combination of deep convolutional generative adversarial networks (DCGANs) and a convolutional network with a high-pass filter (CNHF) is proposed in this paper. Among them, the DCGAN combined with one-dimensional (1D) vibration data converted to a grayscale map is used to expand the fault data to solve the problem of a lack of fault data samples. The CNHF is realized by adding an adaptive high-pass filter to the conventional convolutional layer, and the threshold of the high-pass filter is adaptively set by the 1D convolution according to different data characteristics, thus greatly filtering out the interference of low-frequency noise and realizing the accurate diagnosis of faults. Experiments are performed on a drivetrain dynamics simulator rig to verify the efficacy of the proposed method.
科研通智能强力驱动
Strongly Powered by AbleSci AI