Multivariate Fusion Covariance Matrix Network and Its Application in Multichannel Fault Diagnosis With Fewer Training Samples
提出一种多元融合协方差矩阵网络，利用循环自相关分析分离多通道信号并构建融合协方差矩阵，再输入自动编码器进行故障诊断，在少训练样本下表现优于多种现有方法。
Due to the large number of monitoring variables in engineering, it is extremely to reflect fault information in machinery and equipment with a single channel signal, which poses a significant challenge for fault diagnosis. Furthermore, most existing intelligent recognition methods rely on label samples, yet ignore the high cost of label interpretation in practical engineering. In this work, a novel multivariate fusion covariance matrix network (MFCMN) is developed for multichannel fault diagnosis with fewer training samples. First, the collected multichannel signals are separated into mode functions by using cyclic autocorrelation analysis. Thereafter, the acquired mode functions are utilized to construct the multivariate fusion covariance matrix (MFCM), which retains the linkage of signals from different channels. Finally, MFCM is fed into the standard autoencoder to form the MFCMN network, which is applied to implement multichannel fault diagnosis. To assess effectiveness, the MFCMN is compared with the deep residual network (ResNet), convolutional neural network (CNN), long short-term memory (LSTM), and K-nearest neighbor (KNN) in two experimental cases with fewer training samples. The results clarify that the MFCMN offers excellent performance and high accuracy in multichannel fault diagnosis.