Regional-Local Adversarially Learned One-Class Classifier Anomalous Sound Detection in Global Long-Term Space

Anomalous sound detection (ASD) is one of the most significant tasks of mechanical equipment monitoring and maintaining in complex industrial systems. In practice, it is vital to efficiently identify abnormal status of the working mechanical system, which can further facilitate the failure troubleshooting. In this paper, we propose a multi-pattern adversarial learning one-class classification framework, which allows us to use both the generator and the discriminator of an adversarial model for efficient ASD. The core idea is to learn reconstructing the normal patterns of acoustic data through two different patterns from auto-encoding generators, which succeeds in generalizing the fundamental role of a discriminator from identifying real and fake data to distinguishing between regional and local pattern reconstructions. Moreover, we design a novel balanceable detection strategy using both generators and a discriminator to achieve anomaly detection efficiently. Furthermore, we present a global filter layer for long-term interactions in the frequency domain space, which directly learns from the original data without introducing any human priors. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG, and one existing publicly) for anomaly detection, all showing superior results and outperform recent state-of-the-art ASD methods.