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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...

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Autores principales: Wang, Meng, Shen, Kejun, Tai, Caiwang, Zhang, Qiaofeng, Yang, Zongwei, Guo, Chengbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010560/
https://www.ncbi.nlm.nih.gov/pubmed/36913324
http://dx.doi.org/10.1371/journal.pone.0277352
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author Wang, Meng
Shen, Kejun
Tai, Caiwang
Zhang, Qiaofeng
Yang, Zongwei
Guo, Chengbin
author_facet Wang, Meng
Shen, Kejun
Tai, Caiwang
Zhang, Qiaofeng
Yang, Zongwei
Guo, Chengbin
author_sort Wang, Meng
collection PubMed
description 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.
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spelling pubmed-100105602023-03-14 Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model Wang, Meng Shen, Kejun Tai, Caiwang Zhang, Qiaofeng Yang, Zongwei Guo, Chengbin PLoS One Research Article 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. Public Library of Science 2023-03-13 /pmc/articles/PMC10010560/ /pubmed/36913324 http://dx.doi.org/10.1371/journal.pone.0277352 Text en © 2023 Wang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Meng
Shen, Kejun
Tai, Caiwang
Zhang, Qiaofeng
Yang, Zongwei
Guo, Chengbin
Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model
title Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model
title_full Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model
title_fullStr Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model
title_full_unstemmed Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model
title_short Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model
title_sort research on fault diagnosis system for belt conveyor based on internet of things and the lightgbm model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010560/
https://www.ncbi.nlm.nih.gov/pubmed/36913324
http://dx.doi.org/10.1371/journal.pone.0277352
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