  Failures or breakdowns in factory machinery can be costly to companies, so
there is an increasing demand for automatic machine inspection. Existing
approaches to acoustic signal-based unsupervised anomaly detection, such as
those using a deep autoencoder (DA) or Gaussian mixture model (GMM), have poor
anomaly-detection performance. In this work, we propose a new method based on a
deep autoencoding Gaussian mixture model with hyper-parameter optimization
(DAGMM-HO). In our method, the DAGMM-HO applies the conventional DAGMM to the
audio domain for the first time, with the idea that its total optimization on
reduction of dimensions and statistical modelling will improve the
anomaly-detection performance. In addition, the DAGMM-HO solves the
hyper-parameter sensitivity problem of the conventional DAGMM by performing
hyper-parameter optimization based on the gap statistic and the cumulative
eigenvalues. Our evaluation of the proposed method with experimental data of
the industrial fans showed that it significantly outperforms previous
approaches and achieves up to a 20% improvement based on the standard AUC
score.
