A novel fault detection and diagnosis approach based on orthogonal autoencoders

Davide Cacciarelli, Murat Kulahci

Abstract: Highlights•An unsupervised fault detection and diagnosis scheme based on orthogonal autoencoders is proposed for the monitoring of industrial and chemical processes.•The use of integrated gradients allows for exploration of the bottleneck of the autoencoder, providing candidate variables for the root cause analysis.•The proposed method performs well compared to traditional methods in offering compelling and interpretable results.•The analysis shows how the difference in the way faults are introduced affects the detection and diagnosis performances of principal component analysis-based control charts.

External IDs:doi:10.1016/j.compchemeng.2022.107853