Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model

• Published in: PloS one Vol. 18; no. 3; p. e0277352
• Main Authors: Wang, Meng, Shen, Kejun, Tai, Caiwang, Zhang, Qiaofeng, Yang, Zongwei, Guo, Chengbin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.03.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Belt conveyors; Coal; Coal mines; Coal mining; Coal transport; Computer and Information Sciences; Conveying machinery; Data collection; Data visualization; Decision making; Decision trees; Deformation; Engineering and Technology; Evaluation; Failure; Fault detection; Fault diagnosis; Faults (Geology); Field tests; Friction; Humans; Internet of Things; Machine learning; Medical diagnosis; Mineral industry; Mining industry; Model testing; Modelling; Monitoring systems; Physical Sciences; Production management; Research and Analysis Methods; Sensors; Software; Transportation; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0277352

As an equipment failure that often occurs in coal production and transportation, belt conveyor failure usually requires many human and material resources to be identified and diagnosed. Therefore, it is urgent to improve the efficiency of fault identification, and this paper combines the internet of things (IoT) platform and the Light Gradient Boosting Machine (LGBM) model to establish a fault diagnosis system for the belt conveyor. Firstly, selecting and installing sensors for the belt conveyor to collect the running data. Secondly, connecting the sensor and the Aprus adapter and configuring the script language on the client side of the IoT platform. This step enables the collected data to be uploaded to the client side of the IoT platform, where the data can be counted and visualized. Finally, the LGBM model is built to diagnose the conveyor faults, and the evaluation index and K-fold cross-validation prove the model’s effectiveness. In addition, after the system was established and debugged, it was applied in practical mine engineering for three months. The field test results show: (1) The client of the IoT can well receive the data uploaded by the sensor and present the data in the form of a graph. (2) The LGBM model has a high accuracy. In the test, the model accurately detected faults, including belt deviation, belt slipping, and belt tearing, which happened twice, two times, one time and one time, respectively, as well as timely gaving warnings to the client and effectively avoiding subsequent accidents. This application shows that the fault diagnosis system of belt conveyors can accurately diagnose and identify belt conveyor failure in the coal production process and improve the intelligent management of coal mines.