基于物联网和LightGBM模型的带式输送机故障诊断系统研究,PLOS ONE

当前位置： X-MOL 学术 › PLOS ONE › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

基于物联网和LightGBM模型的带式输送机故障诊断系统研究
PLOS ONE ( IF 2.9 ) Pub Date : 2023-03-13 , DOI: 10.1371/journal.pone.0277352
Meng Wang ₁ , Kejun Shen ₁ , Caiwang Tai ₂ , Qiaofeng Zhang ₁ , Zongwei Yang ₁ , Chengbin Guo ₃

Affiliation

带式输送机故障作为煤炭生产和运输中经常发生的设备故障，通常需要耗费大量的人力、物力进行识别和诊断。因此，提高故障识别效率迫在眉睫，本文结合物联网（IoT）平台和光梯度提升机（LGBM）模型建立带式输送机故障诊断系统。首先，为带式输送机选装传感器，采集运行数据。其次，在物联网平台客户端连接传感器和Aprus适配器并配置脚本语言。该步骤可以将采集到的数据上传到物联网平台客户端，实现数据统计和可视化。最后，建立LGBM模型对输送机故障进行诊断，评价指标和K折交叉验证证明了模型的有效性。此外，系统搭建调试完成后，在实际矿山工程中应用了三个月。现场测试结果表明：（1）物联网客户端能够很好地接收传感器上传的数据，并以图表的形式呈现数据。(2) LGBM 模型精度高。测试中，模型准确检测出皮带跑偏、皮带打滑、皮带撕裂等故障，分别发生2次、2次、1次、1次，并及时向客户发出预警，有效避免后续事故的发生.

"点击查看英文标题和摘要"

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

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.

更新日期：2023-03-13

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南