Health Monitoring of Industrial machines using Scene-Aware Threshold Selection
This work addresses anomaly detection for industrial maintenance, but it is incremental as it builds on existing autoencoder methods by adding adaptive thresholding.
The paper tackles the problem of anomaly detection in industrial machines using sound by proposing an autoencoder-based unsupervised approach with adaptive threshold selection based on scene classification, resulting in significant performance improvement over fixed thresholds on the MIMII dataset.
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.