Abstract
As one of the key components of the belt conveyor, the roller bears the task of supporting and rolling the conveyor belt, and monitoring its condition is very important. The traditional monitoring of the conveyor roller group adopts worker inspection, which has strong subjectivity. Monitoring using sensors necessitates the use of numerous sensors, which can pose wiring challenges. The use of inspection robots for monitoring results can be discontinuous, and their performance may be limited. This study proposes a fault diagnosis method for rollers based on a distributed fiber optic sensing system. By improving the traditional Isolation Forest (IForest), a framework called Incremental Majority Voting Isolation Forest (IMV-IForest) is proposed. By analyzing the optical signal, we extracted the variation patterns of roller faults over time and space, and analyzed the abnormal score distribution between fault data and normal data. Using the dataset collected on-site, we compared and analyzed IMV-IForest with the traditional IForest and the Extended Isolation Forest (E-iForest). The results indicate that the variation of the fault of the faulty roller with time and space can be used for early prediction of roller faults; determine an anomaly score threshold of 0.6; improved IForest have faster computation time and higher accuracy. Finally, to verify the effectiveness of the proposed scheme, a 3-month experiment was conducted on a 600 m long belt conveyor in a certain mine, and on-site monitoring results were obtained. By comparing with manual detection results, it was shown that the proposed method has high recognition rate for faulty idlers, with an accuracy rate of 97.92%, and can effectively diagnose faulty idlers.