Acoustic Feature Extraction Based on Wavelet Transform for Industrial Induction Motor-Driven Belt Conveyor Condition Monitoring

The industrial induction motor-driven belt conveyor is an essential component in manufacturing facilities. Any unexpected shutdown can lead to significant disruptions, resulting in financial losses amounting to thousands of dollars per hour. Unfortunately, efficient mechanisms for monitoring the conveyor's condition are often lacking. Therefore, it is crucial to ensure early, precise, and effective detection of malfunctions in belt conveyors. This necessitates the identification of distinctive anomalies stemming from initial damage to the rotating machinery and motor components. This paper presents a non-invasive acoustic monitoring technique designed specifically for industrial belt conveyors. The method employed relies on wavelet transform-based feature extraction, offering notable advantages in terms of classification accuracy, time efficiency, and the quantity of feature vectors required for classifier training.