On-device Anomaly Detection in Conveyor Belt Operations
This work addresses the critical need for robust, real-time monitoring of mining conveyor belts to improve productivity by detecting failures, which is an incremental advance over existing methods.
The study tackled the problem of on-device anomaly detection in conveyor belt operations by proposing two novel methods for classifying normal and abnormal duty cycles, achieving F1-scores up to 97.3% for normal cycles and 80.2% for abnormal cycles in one dataset, and 91.3% for normal cycles and 67.9% for abnormal cycles in another, with energy consumption as low as 13.3 μJ during inference.
Conveyor belts are crucial in mining operations by enabling the continuous and efficient movement of bulk materials over long distances, which directly impacts productivity. While detecting anomalies in specific conveyor belt components has been widely studied, identifying the root causes of these failures, such as changing production conditions and operator errors, remains critical. Continuous monitoring of mining conveyor belt work cycles is still at an early stage and requires robust solutions. Recently, an anomaly detection method for duty cycle operations of a mining conveyor belt has been proposed. Based on its limited performance and unevaluated long-term proper operation, this study proposes two novel methods for classifying normal and abnormal duty cycles. The proposed approaches are pattern recognition systems that make use of threshold-based duty-cycle detection mechanisms, manually extracted features, pattern-matching, and supervised tiny machine learning models. The explored low-computational models include decision tree, random forest, extra trees, extreme gradient boosting, Gaussian naive Bayes, and multi-layer perceptron. A comprehensive evaluation of the former and proposed approaches is carried out on two datasets. Both proposed methods outperform the former method in anomaly detection, with the best-performing approach being dataset-dependent. The heuristic rule-based approach achieves the highest F1-score in the same dataset used for algorithm training, with 97.3% for normal cycles and 80.2% for abnormal cycles. The ML-based approach performs better on a dataset including the effects of machine aging, with an F1-score scoring 91.3% for normal cycles and 67.9% for abnormal cycles. Implemented on two low-power microcontrollers, the methods demonstrate efficient, real-time operation with energy consumption of 13.3 and 20.6 \textmu J during inference. These results ...