Abstract
In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment. We propose a comprehensive feature learning approach that operates on raw audio, by supervising the formation of salient audio embeddings in latent states of a deep temporal convolutional neural network. By fusing the supervised feature learning approach with an unsupervised deep one-class neural network, we are able to model the characteristics of each source and implicitly detect anomalies in different operational states of industrial machines. Moreover, we enable the exploitation of spatial audio information in the learning process, by formulating a novel front-end processing strategy for circular microphone arrays. Experimental results on the MIMII dataset demonstrate the effectiveness of the proposed method, reaching a state-of-the-art mean AUC score of 91.0%. Anomaly detection performance is significantly improved by incorporating multi-channel audio data in the feature extraction process, as well as training the convolutional neural network on the spatially invariant front-end. Finally, the proposed semi-supervised approach allows the concise modeling of normal machine conditions and accurately detects system anomalies, compared to existing anomaly detection methods.
Highlights
Mechanical equipment usually operates while exposed to hazardous or otherwise challenging working environments, which happen to affect its reliability and can cause system breakdowns with significant safety and economic impact [1,2]
Instead of using a direct approach to anomaly detection, which is to model a one-class classifier on normal class samples, we introduce a two-stage method that provides the one-class classifier with dynamically extracted feature vectors
The performance of the models is validated against two unsupervised anomaly detection models from recent works, which operate on the same dataset and configuration
Summary
Mechanical equipment usually operates while exposed to hazardous or otherwise challenging working environments, which happen to affect its reliability and can cause system breakdowns with significant safety and economic impact [1,2]. Continuous monitoring and periodic manual inspections are essential practices to prevent any potential issues and ensure the proper maintenance of the equipment, facilitating the operational continuity of industrial production [3]. Automatic machine condition monitoring has long attracted the interest of researchers and engineers, anticipating the development of intelligent and generic methods to promptly detect and diagnose faults in mechanical equipment [4]. Real-world industrial conditions pose great challenges to automatic failure detection, as surrounding industrial noise may lead to a low signal-to-noise ratio and eventually impair the performance of audio-driven CM systems [7]. Improvements in automatic machine condition monitoring can be expected, due to the significant progress demonstrated by data-driven and deep learning methods in application areas that can generate massive amounts of data [8,9]
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