Equipment anomaly detection method under cloud-edge collaboration model based bi-directional long short-term memory and variational autoencoder

Abstract

Aiming at the problems of large amount of heterogeneous Industrial data, high fault concealment, complex feature engineering of traditional methods, an anomaly detection method combined with Bi-directional long-short term memory, variational autoencoder and whale optimization algorithm based on cloud-edge collaboration. By integrating the output of each detection models with different dimensions through the residual weight matrix, to obtain the comprehensive residual value, and compare with the residual threshold for anomaly detection. Through the experiments on SKAB and TEP datasets, the results verify the effectiveness and general adaptability of the proposed method, and the anomaly detection accuracy is higher than a single detection model and existing anomaly algorithms such as CNN and LSTM-AE.