采用多变量耦合网络与变分图自编码器的机械设备异常检测方法

To comprehensively and accurately detect potential equipment abnormalities, the multi-dimensional and multi-state monitoring data generated by multi-point and multi-source sensors in highly correlated and coupled distributed systems, such as steam turbines, turbines, and wind power rotors are taken into account, and an anomaly detection method with multivariable coupling network and variational graph autoencoder is proposed. The detrended cross-correlation analysis (DCCA) method is used to quantitatively analyze the coupling relationship between multi-dimensional variables, and then a system multivariable coupling relationship network is constructed. Then a variational graph autoencoder based on unsupervised learning is established, and feature extraction on the system multivariable coupling relationship network is performed. The normal data are chosen to train the model, the graph convolutional network is taken as the encoder to learn the distribution of input data, and the potential representation is obtained via sampling to realize the reconstruction of the coupled network. The reconstruction probability is used as the system anomaly detection index. Taking an example, the anomaly detection analysis is carried out with the monitoring data of turbine rotor system in a thermal power plant. It reveals that considering the coupling relationship between multi-dimensional polymorphic data, the accuracy and reliability of the system anomaly detection are improved; the unsupervised learning method with variational graph autoencoder is able to reduce the empirical dependence and solve the problem of insufficient abnormal samples.