Unsupervised Machine Anomaly Detection Using Autoencoder and Temporal Convolutional Network
Zhiyuan Li, Yu Sun, Laihao Yang, Zhibin Zhao, Xuefeng Chen
Abstract
Anomaly detection is the cornerstone of the health management of much large industrial mechanical equipment. Most machinery anomaly detection methods try to find a variable threshold to indicate whether machinery is functioning normally. However, due to the scarcity of anomalous data, how to drive anomaly detection with variable threshold only by normal data remains a significant challenge. This is because, in practice, mechanical equipment’s operating speed and load conditions are constantly changing. To solve this problem, an unsupervised anomaly detection approach based on autoencoders and temporal convolutional networks was proposed in this article, which is named variable cumulative error anomaly detection (VCEAD). The autoencoder will be used to compute the signal reconstruction error, and the temporal convolutional network will be used to predict the vibration signal. Finally, an algorithm based on the probability distribution of the signal reconstruction error will be proposed to calculate the so-called variable cumulative error. To validate the effectiveness of the proposed method in anomaly detection, multigroup lifetime datasets collected from the laboratory were studied and analyzed. The comparative results show that the method presented in this article achieves superior performance.