  This paper aims to develop an acoustic signal-based unsupervised anomaly
detection method for automatic machine monitoring. Existing approaches such as
deep autoencoder (DAE), variational autoencoder (VAE), conditional variational
autoencoder (CVAE) etc. have limited representation capabilities in the latent
space and, hence, poor anomaly detection performance. Different models have to
be trained for each different kind of machines to accurately perform the
anomaly detection task. To solve this issue, we propose a new method named as
hierarchical conditional variational autoencoder (HCVAE). This method utilizes
available taxonomic hierarchical knowledge about industrial facility to refine
the latent space representation. This knowledge helps model to improve the
anomaly detection performance as well. We demonstrated the generalization
capability of a single HCVAE model for different types of machines by using
appropriate conditions. Additionally, to show the practicability of the
proposed approach, (i) we evaluated HCVAE model on different domain and (ii) we
checked the effect of partial hierarchical knowledge. Our results show that
HCVAE method validates both of these points, and it outperforms the baseline
system on anomaly detection task by utmost 15 % on the AUC score metric.
