Abstract:
Failures or breakdowns in factory machinery can cause a significant cost to companies. Therefore, there is an increasing demand for automatic machine inspection. In this work, our aim is to develop an acoustic signal based unsupervised anomaly detection method. Existing approaches such as deep autoencoder (DA) and Gaussian mixture model (GMM) have poor anomaly-detection performance. We propose a new method based on deep autoencoding Gaussian mixture model with hyper-parameter optimization (DAGMM-HO). The DAGMM-HO applies the conventional DAGMM to the audio domain for the first time, expecting that its total optimization on reduction of dimensions and statistical modelling improves anomaly-detection performance. In addition, the DAGMM-HO solves the hyper-parameter sensitivity problem of the conventional DAGMM by hyper-parameter optimization based on the gap statistic and the cumulative eigenvalues. We evaluated the proposed method with experimental data of the industrial fans. We found that it significantly outperforms previous approaches, and achieves up to a 20% improvement based on the standard AUC score.
