ANOMALY DETECTION IN INDUSTRIAL MACHINES USING EXPLAINABLE AI AND ACOUSTIC SIGNALS

Abstract

In recent years, anomaly detection in industrial machinery has become a critical topic aimed at preventing failures, improving maintenance processes, and enhancing production efficiency. As industrial machines operate continuously under varying conditions, early detection of anomalies can reduce unplanned delays and maintenance costs. Acoustic signals are important data sources for evaluating the health of industrial equipment. This study proposes a lightweight machine learning (ML) approach for anomaly detection in industrial machines using acoustic signals. For this purpose, audio features are extracted, and their statistical metrics are calculated to define a new dataset for anomaly detection. Explainable Artificial Intelligence (XAI) techniques are employed to determine the most effective features that contribute to anomaly detection of machines. The proposed method tested using the publicly available MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection. The results demonstrate that the proposed approach achieves highly promising outcomes, with an accuracy of 99.7% and an AUC score of 99.9% in classifying anomalies in industrial machines.