School of Key Laboratory of Vibration and Noise under Ministry of Education of Yunnan Province, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel). 2022 Mar 30;22(7):2641. doi: 10.3390/s22072641.
The rotate vector (RV) reducer has a complex structure and highly coupled internal components. Acoustic emission (AE) signal, which is more sensitive to a weak fault, is selected for fault diagnosis of the RV reducer. The high sampling frequency and big data are the challenges for AE signal store and analysis. This study combines compressed sensing (CS) and convolutional neural networks. As a result, data redundancy is significantly reduced while retaining most of the information, and the analysis efficiency is improved. Firstly, the time-domain AE signal was projected into the compression domain to obtain the compression signal; then, the wavelet packet decomposition in the compressed domain was performed to obtain the information of each frequency band. Next, the frequency band information was sent into the input layer of the multi-channel convolutional layer, and the energy pooling layer mines the energy characteristics of each frequency band. Finally, the softmax classifier was used to classify and predict different fault types of RV reducers. The self-fabricated RV reducer experimental platform was used to verify the proposed method. The experimental results show that the proposed method can effectively extract the fault features in the AE signal of the RV reducer, improve the efficiency of signal processing and analysis, and achieve the accurate classification of RV reducer faults.
旋转矢量（RV）减速器结构复杂，内部组件高度耦合。声发射（AE）信号对微弱故障更为敏感，因此被选作 RV 减速器的故障诊断方法。高采样频率和大数据量给 AE 信号存储和分析带来了挑战。本研究结合了压缩感知（CS）和卷积神经网络。结果，在保留大部分信息的同时，大大减少了数据冗余，提高了分析效率。首先，对时域 AE 信号进行投影，将其转换到压缩域，得到压缩信号；然后，在压缩域中进行小波包分解，获取各频带的信息。接下来，将频带信息输入多通道卷积层的输入层，利用能量池化层挖掘各频带的能量特征。最后，使用 softmax 分类器对 RV 减速器的不同故障类型进行分类和预测。使用自制的 RV 减速器实验平台验证了所提出的方法。实验结果表明，所提出的方法可以有效地提取 RV 减速器 AE 信号中的故障特征，提高信号处理和分析的效率，并实现 RV 减速器故障的准确分类。
