Method and system for an acoustic based anomaly detection in industrial machines

A system for an acoustic based anomaly detection in an industrial machine comprising:

• an input/output interface;
• one or more hardware processors;
• a memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory, to:
• receive a mixed audio signal of one or more spatially distributed audio sources via a microphone array, wherein the mixed audio signal includes noises;
• beamform the received mixed audio signal using a delay-and-sum beamforming technique to obtain a constructive superposition of the audio signal along a predefined direction;
• estimate at least one clean audio source of the one or more spatially distributed audio sources using a pretrained sequential transform learning (STL), wherein the STL is used to model dynamics of the time-varying source signals for source separation;
• determine a change in the estimated at least one clean audio source of the one or more spatially distributed audio sources obtained using STL and beamforming by comparing with a template of a normal audio source of the same industrial machine;
• and
• detect one or more abnormalities in the industrial machines based on the change determined in the associated clean audio source that is below a predefined threshold in terms of signal to noise ratio (SNR).

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause an acoustic based anomaly detection in an industrial machine comprising:

• receiving, via a microphone array, a mixed audio signal of one or more spatially distributed audio sources, wherein the mixed audio signal includes noises;
• beamforming the received mixed audio signal using a delay-and-sum beamforming technique to obtain a constructive superposition of the audio signal along a predefined direction;
• estimating at least one clean audio source of the one or more spatially distributed audio sources using a pre-trained sequential transform learning (STL), wherein the STL is used to model dynamics of the time-varying source signals for source separation;
• determining a change in the estimated at least one clean audio source of the one or more spatially distributed audio sources obtained using STL and beamforming by comparing with a template of a normal audio source of the same industrial machine;
• and
• detecting one or more abnormalities in the industrial machine based on the change determined in the associated clean audio source that is below a predefined threshold in terms of signal to noise ratio (SNR).