Health Monitoring of Industrial machines using Scene-Aware Threshold Selection

TL;DR

This paper introduces an adaptive threshold method for anomaly detection in industrial machines using sound analysis, improving detection accuracy across varying environmental conditions by classifying surrounding scenes.

Contribution

It proposes a scene-aware adaptive threshold selection framework for unsupervised machine health monitoring using sound-based autoencoder models.

Findings

Adaptive threshold improves detection accuracy

Scene classification enables environment-independent anomaly detection

Method tested successfully on MIMII dataset

Abstract

This paper presents an autoencoder based unsupervised approach to identify anomaly in an industrial machine using sounds produced by the machine. The proposed framework is trained using log-melspectrogram representations of the sound signal. In classification, our hypothesis is that the reconstruction error computed for an abnormal machine is larger than that of the a normal machine, since only normal machine sounds are being used to train the autoencoder. A threshold is chosen to discriminate between normal and abnormal machines. However, the threshold changes as surrounding conditions vary. To select an appropriate threshold irrespective of the surrounding, we propose a scene classification framework, which can classify the underlying surrounding. Hence, the threshold can be selected adaptively irrespective of the surrounding. The experiment evaluation is performed on MIMII dataset for industrial machines namely fan, pump, valve and slide rail. Our experiment analysis shows that utilizing adaptive threshold, the performance improves significantly as that obtained using the fixed threshold computed for a given surrounding only.