The parameters of sensor technical.

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.

作为煤炭生产与运输中频发的设备故障类型，带式输送机故障通常需要耗费大量人力物力才能完成识别与诊断。因此，提升故障识别效率迫在眉睫。本文结合物联网（IoT）平台与轻量梯度提升机（Light Gradient Boosting Machine，LGBM）模型，构建了带式输送机故障诊断系统。首先，针对带式输送机选取并安装传感器以采集其运行数据；其次，将传感器与Aprus适配器相连，并在物联网平台客户端配置脚本语言，该步骤可实现采集数据上传至物联网平台客户端，进而完成数据统计与可视化展示；最后，构建LGBM模型以实现输送机故障诊断，并通过评估指标与K折交叉验证验证了该模型的有效性。此外，该系统搭建调试完成后，在实际矿山工程中开展了为期三个月的应用测试。现场测试结果表明：（1）物联网客户端可稳定接收传感器上传的数据，并以图表形式呈现数据；（2）LGBM模型具备较高的识别精度。测试中，该模型精准检测出带式跑偏、打滑、撕裂四类故障，对应发生次数分别为2次、2次、1次与1次，并及时向客户端发出告警，有效规避了后续事故的发生。本次应用验证表明，所提出的带式输送机故障诊断系统可在煤炭生产过程中精准诊断并识别带式输送机故障，提升煤矿智能化管理水平。