Reconstruction error‐based fault detection of time series process data using generative adversarial auto‐encoders

Abstract

Faults in time series process data are typically difficult to detect due to the complex temporal correlations of data samples. In this context, traditional unsupervised machine learning algorithms, such as principal component analysis, independent component analysis, and so forth, would yield only limited performance. Deep learning‐based methods have been employed in recent years to address these problems. Recently, generative adversarial networks have emerged as a promising generative modelling approach for learning data distributions. Inspired by the above, in this study, we present a novel reconstruction error‐based fault detection method for time series process data using generative adversarial auto‐encoder (GAAE) with Wasserstein loss, cycle consistency loss, and gradient penalty methods. The proposed method is designed to detect abnormal patterns in the time series data by training a GAAE to learn the underlying normal behaviour of the data. GAAEs help in effectively capturing the data's hidden distribution, and Wasserstein loss with gradient penalty is used to improve the accuracy of the latent space representation of the data, while the cycle consistency loss ensures consistency between the input and output data during the reconstruction process. Based on the extent of the reconstruction error metric of the GAAEs, we identify the potential faults in the process data stream. The proposed method is evaluated on two data process sets, namely, the Tennessee Eastman benchmark process dataset and the nuclear power flux real‐time dataset from a pressurized heavy water nuclear reactor, to validate the efficacy of the proposed approach.