A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder

Abstract

During the operation of rotating machinery, the occurrence of unknown fault types makes it impossible for the artificial intelligence-based fault diagnosis model to distinguish. Furthermore, due to the excessive generalization capability of the autoencoder, the unsupervised anomaly detection method based on the autoencoder is difficult to effectively distinguish normal and abnormal samples. To address the above problem, this paper proposed an unsupervised anomaly detection method based on memory augmented temporal convolutional autoencoder (MATCAE). Firstly, a novel temporal convolutional autoencoder model is constructed based on dilated causal convolution, skip connection and autoencoder to facilitate the model to learn the temporal features of the input data, thereby enhancing the model’s ability to capture the complex structure of the data. Then, a memory augmented module is designed using a memory matrix and an attention mechanism to expand the distribution interval between the reconstructed samples of normal and abnormal samples and reduce the sample capacity in the overlapping area. Finally, an anomaly detection module based on Euclidean distance, cosine distance and absolute mean square error is designed to improve the reliability of the metric between the input and reconstructed samples. To verify the effectiveness of the proposed method, experimental validation is carried out on a gearbox anomaly detection dataset. The experimental results show that the proposed method has higher anomaly detection accuracy and better noise robustness than other advanced anomaly detection methods, where the average performance metric is improved by 26.86% at the highest and 2.80% at the lowest.