Notice of Removal: Acoustic based Machine Anomaly Detection using Beamforming and Sequential Transform Learning

Abstract

Acoustic-based analysis has been widely used for the maintenance and operation of industrial machines. However, interferences and background noise highly contaminate the observed acoustic signal. Here, we present a novel two-stage multi-channel source separation technique for improved separation and robust anomaly detection. Beamforming is applied in the first stage to provide separation at a coarser level. Sequential transform learning is employed in the second stage to learn the dynamics of the time-varying source signal for more refined source separation. The separated machine sounds are analyzed for anomaly using a simple template matching approach. Results obtained using MIMII dataset demonstrate the superior performance of the proposed method for source separation and anomaly detection compared to other state-of the-art techniques.