Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients. (June 2022) - Peng, Peng; Zhang, Yi; Wang, Hongwei; Zhang, Heming
Abstract: Industrial applications of fault detection and diagnosis face great challenges as they require not only accurate identification of faulty statuses but also the effective understandability of the results. In this paper, a two-step robust and understandable fault detection and diagnosis framework is developed to address this challenge by exploiting denoising sparse autoencoder and smooth integrated gradients. Specifically, denoising sparse autoencoder(DSAE) is utilized to detect faults in the first step. DSAE is more robust to noise corruption and has better generalization performance compared to the existing autoencoder-based methods. In the second step, smooth integrated gradients(SIG) is used to diagnose the root-cause variables of the faults detected. Smooth integrated gradients can provide a denoising effect on the feature importance. The proposed framework is evaluated through an application to the Tennessee Eastman process. As proved in the experiments, the presented DSAE-SIG method not only achieves higher diagnosis accuracy but also successfully identifies the potential root-cause variables of process disturbances. Highlights: A fault detection method is developed based on denoising sparse autoencoder, which is more robust to environmental noises and has better generalization performance. A method for understanding the input–output mapping and correlation in denoising sparse autoencoder is devised through incorporating smooth integrated gradients. An understandable fault diagnosis framework based on the two methods is developed for finding out the root cause of faults detected. Effectiveness of the framework is demonstrated through computational experiments conducted on the Tennessee Eastman Process dataset.