MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing

by   Lucas Correia, et al.

A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reaches its capacity. Such real-world data is massive, diverse, multivariate and temporal in nature, therefore requiring modelling of the testee behaviour. We propose a variational autoencoder with multi-head attention (MA-VAE), which, when trained on unlabelled data, not only provides very few false positives but also manages to detect the majority of the anomalies presented. In addition to that, the approach offers a novel way to avoid the bypass phenomenon, an undesirable behaviour investigated in literature. Lastly, the approach also introduces a new method to remap individual windows to a continuous time series. The results are presented in the context of a real-world industrial data set and several experiments are undertaken to further investigate certain aspects of the proposed model. When configured properly, it is 9 discovers 67 perform well with only a fraction of the training and validation subset, however, to extract it, a more sophisticated threshold estimation method is required.


Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things

In this paper, we propose Squeezed Convolutional Variational AutoEncoder...

NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection

In recent studies, Lots of work has been done to solve time series anoma...

Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models

This paper introduces a new methodology for detecting anomalies in time ...

Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Anomaly detection has been a challenging task given high-dimensional mul...

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis

Anomaly detection in database management systems (DBMSs) is difficult be...

Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection

We propose a new method for testing antenna arrays that records the radi...

A VAE Approach to Sample Multivariate Extremes

Generating accurate extremes from an observational data set is crucial w...

Please sign up or login with your details

Forgot password? Click here to reset