Quantile Autoencoder With Abnormality Accumulation for Anomaly Detection of Multivariate Sensor Data

Abstract

Anomaly detection (AD) is a crucial task in various industrial sectors where large amounts of data are generated from multiple sensors. Deep learning-based methods have made significant progress in AD, owing to big data and deep neural networks (DNN). Most methods for deep anomaly detection (DAD) utilize reconstruction error (i.e., the difference between the original and reconstructed values) as a measure of abnormality. However, AD performance can be improved by diversifying the source of anomaly score. To support this, we introduce the concept of anomaly source diversification and provide mathematical proofs to support this idea. In this regard, we propose a quantile autoencoder (QAE) with abnormality accumulation (AA) as a novel DAD approach that leverages data uncertainty and iteratively obtains reconstruction errors as additional sources. The anomaly score with QAE is derived from both the reconstruction error and the uncertainty term which is the range between the two quantiles. In addition, AA aggregates the errors obtained from the recursive reconstruction of the input, after which calculates the anomaly score based on the Mahalanobis distance. This process induces the score distributions of both the normal and abnormal samples farther apart by narrowing the width of the distributions, which contributes to the improvement of AD performance. The performance of the proposed QAE-AA was verified through the experiments on multi-variate sensor datasets in various domains; QAE-AA achieves 4-23% higher AUROC score on average compared to the other AD methodologies.