An anomalous sound detection methodology for predictive maintenance. (15th December 2022) - Di Fiore, Emanuele; Ferraro, Antonino; Galli, Antonio; Moscato, Vincenzo; Sperlì, Giancarlo
Abstract: 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. Highlights: Machine audio signal has been analyzed by our methodology for anomaly detection task. Flexibility of our methodologies allows to integrate any type of autoencoder. Our approach has been evaluated on real dataset in terms of efficiency and efficacy. We evaluate our methodology in real-time scenario having been tested on real dataset.