An On-Device Hybrid Machine Learning Approach for Anomaly Detection in Conveyor Belt Operations

The mining sector harnesses advancements in automation, digitalization, and interconnected technologies from Industry 4.0 to enhance efficiency, safety, and sustainability. Conveyor belts play a critical role in mining operations, facilitating the continuous and efficient transport of bulk materials over long distances, directly impacting productivity. While anomaly detection in specific conveyor belt components has been extensively studied, continuous monitoring to identify root causes of failures remains in its early stages. Existing methods for anomaly detection in mining conveyor belt duty cycles rely on supervised machine learning (ML) to classify internal machine modes as an intermediate step. While these approaches offer high explainability, they are constrained by the need for extensive labeled data for internal machine modes. This study proposes a novel pattern recognition approach combining unsupervised and supervised ML models for real-time anomaly detection in conveyor belt operational cycles. By evaluating combinations of TinyML models, the approach achieved average F1-scores of 83.2% for abnormal cycles and 97.0% for normal cycles, surpassing the state-of-the-art by 11.4% and 3.3%, respectively. Deployed on low-power microcontrollers, the proposed methods demonstrated efficient, real-time operation, reducing energy consumption by up to 84.5% (4.1 µJ per inference) and program memory usage by up to 72.1%. These results provide valuable insights for detecting early mechanical failures and enabling targeted preventive maintenance.