Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders

Abstract

Data-driven fault diagnostics of safety-critical systems often faces the challenge of a complete lack of labeled data from faulty system conditions at training time. Since faults of unknown types can arise during deployment, fault diagnostics in this scenario is an open-set learning problem. Without labels and samples from the possible fault types, the open-set diagnostics problem is typically reformulated as fault detection and fault segmentation tasks. Traditional approaches to these tasks, such as one-class classification and unsupervised clustering, do not typically leverage all the available labeled and unlabeled data in the learning algorithm. As a result, their performance is sub-optimal. In this work, we propose an adapted version of the variational autoencoder (VAE), which leverages all available data at training time and has two new design features: 1) implicit supervision on the latent representation of the healthy conditions and 2) implicit bias in the sampling process. The proposed method induces a compact and informative latent representation, thus enabling good detection and segmentation of previously unseen fault types. In an extensive comparison using two turbofan engine datasets, we demonstrate that the proposed method outperforms other learning strategies and deep learning algorithms, yielding significant performance improvements in fault detection and fault segmentation.