Unsupervised Anomaly Detection in Predictive Maintenance using Sound Data

This paper represent an extended abstract of a recent proposal ([1]), in which the authors present a new methodology for unsupervised anomaly detection in predictive maintenance using sound data. In particular, the methodology leverages LSTM and CNN-based autoencoders to process continuous audio streams from different audio sources in real-world factories based on a customized sliding window. The novelties of the proposed approach include a general methodology for unsupervised anomaly detection, machine ID encoding using one-hot encoding, and conditioning an autoencoder by jointly analyzing the relationships between the mel-spectrogram and the machine ID to compute an anomaly score. The methodology achieves good performances in terms of effectiveness and in addition low inference time and memory requirements.