Identification of Belt Conveyor Malfunctions Using Machine Learning with Diverse Anomalous Sound Data

This study introduces a novel approach to identifying belt conveyor malfunctions through the application of machine learning techniques, utilizing diverse anomalous sound data as training input. Traditional methods often rely on specific fault sound data for training, limiting their adaptability to unforeseen anomalies. In contrast, the methodology of this study utilizes machine learning algorithms trained on various abnormal sounds such as glass breaking or screams to identify malfunctions of the belt conveyor. The study focuses on the collection and curation of a diverse dataset encompassing various anomalous sounds, ensuring a comprehensive representation of potential disturbances in an industrial setting. Machine learning models, including deep neural networks, are trained on this heterogeneous dataset, enabling them to recognize abnormal patterns associated with belt conveyor faults. Experimental results demonstrate the efficacy of the proposed approach in accurately identifying belt conveyor malfunctions, even when the learned models are exposed to novel and previously unseen anomalies. The versatility of the method showcases its potential for real-world applications where conventional fault-specific training data may be insufficient. This study not only contributes to the advancement of anomaly detection in industrial settings but also highlights the importance of leveraging diverse sound data for enhanced machine learning-based fault identification.