An anomalous sound detection methodology for predictive maintenance

In the last decade, Anomalous Sound Detection (ASD) is becoming an increasingly challenging task for a plethora of applications due to the widespread diffusion of Deep Neural Networks. Nevertheless, the arise of recent cyber-physical attacks (i.e. Triton or Stuxnet), that deceive monitoring platforms, pose novel and challenging issues. For this reason, advanced predictive maintenance techniques are starting to exploit sounds generated by particular industrial equipment, whose analysis can unveil symptom of possible failures. For this kind of context, it is very easy to collect data related to normal and abnormal behavior of a given machinery, thus several kinds of deep neural architectures can be effectively trained to predict eventual downtime situations. In this paper, we propose a novel deep learning-based methodology for anomalous sound detection task, having flexibility, modularity and efficiency characteristics. The proposed methodology analyzes audio clips based on the mel-spectogram and ID equipment information, while a one-hot encoding method extracts features that are, successively, used to train an ID Conditioned Network. In particular, the main novelty of the proposed methodology concerns the conditioning of an autoencoder by jointly analyzing the relationships between mel-spectogram and the related machine identifier through an encoder-decoder architecture for computing an anomaly score related to the input sequence. Several experiments have been made for investigating the efficiency and effectiveness of the proposed methodology on multiple instances of different industrial machines (pumps, valves, slide rails and fans), achieving low inference time and memory requirements w.r.t. the other approaches in the literature.

Automated summary

The paper proposes a novel deep learning-based methodology for anomalous sound detection, which is characterized by flexibility, modularity, and efficiency. The methodology calculates an anomaly score by jointly analyzing audio features and machine identifier information, and its efficiency and effectiveness are demonstrated through multiple experiments.