College of Safety Science & Engineering, Liaoning Technical University, Huludao, Liaoning, China.
Key Laboratory of Mine Thermo-Motive Disaster & Prevention, Ministry of Education, Huludao, Liaoning, China.
PLoS One. 2022 Sep 30;17(9):e0275437. doi: 10.1371/journal.pone.0275437. eCollection 2022.
The resistance variant faults (RVFs) observed in the mine ventilation system can utterly restrict mine safety production. Herein, a machine learning model, which is based on multi-label k-nearest neighbor (ML-KNN), is proposed to solve the problem of the rapid and accurate diagnosis of the RVFs that occur at multiple locations within the mine ventilation system. The air volume that passes through all the branches of the ventilation network, including the residual branches, was used as the diagnostic model input after the occurrence of multiple faults, whereas the label vector of the fault locations was used as the model's output. In total, seven evaluation indicators and 1800 groups of randomly simulated faults at the typical locations in a production mine with 153 nodes and 223 branches were considered to evaluate the feasibility of the proposed model to solve for multiple fault locations diagnostic and verify the model's generalization ability. After ten-fold cross-validation of the training sets containing 1600 groups of fault instances, the diagnostic accuracy of the model tested with the air volume of all 223 branches and the 71 residual branches' air volume as input was 73.6% and 72.3%, respectively. On the other hand, To further evaluate the diagnostic performance of the model, 200 groups of the multiple fault instances that were not included in the training were tested. The accuracy of the fault location diagnosis was 76.5% and 73.5%, and the diagnostic time was 9.9s and 12.16s for the multiple faults instances with all 223 branches' air volume and the 71 residual branches' air volume as observation characteristics, respectively. The data show that the machine learning model based on ML-KNN shows good performance in the problem of resistance variant multiple fault locations diagnoses of the mine ventilation system, the multiple fault locations diagnoses can be carried out with all the branches' air volume or the residual branches' air volume as the input of the model, the diagnostic average accuracy is higher than 70%, and the average diagnosis time is less than one minute. Hence, the proposed model's diagnostic accuracy and speed can meet the engineering requirements for the diagnosis of multiple fault locations for a real ventilation system in the field, and this model can effectively replace personnel to discover ventilation system failures, and also lays a good foundation for the construction of intelligent ventilation systems.
在矿井通风系统中观察到的阻力变体故障（RVF）会严重限制矿山安全生产。本文提出了一种基于多标签 K-最近邻（ML-KNN）的机器学习模型，用于解决矿井通风系统中多个位置快速准确诊断 RVF 的问题。在多个故障发生后，使用通风网络中所有分支（包括剩余分支）的风量作为诊断模型的输入，而故障位置的标签向量则作为模型的输出。总共有 7 个评估指标和 1800 组在一个有 153 个节点和 223 个分支的生产矿山典型位置随机模拟的故障，用于评估所提出的模型解决多个故障位置诊断的可行性，并验证模型的泛化能力。在包含 1600 组故障实例的训练集进行十折交叉验证后，使用所有 223 个分支和 71 个剩余分支的风量作为输入的模型测试的诊断准确率分别为 73.6%和 72.3%。另一方面，为了进一步评估模型的诊断性能，对 200 组未包含在训练中的多故障实例进行了测试。使用所有 223 个分支的风量和 71 个剩余分支的风量作为观测特征的多故障实例的故障位置诊断准确率分别为 76.5%和 73.5%，诊断时间分别为 9.9s 和 12.16s。数据表明，基于 ML-KNN 的机器学习模型在矿井通风系统阻力变体多故障位置诊断问题上表现出良好的性能，可以使用所有分支的风量或剩余分支的风量作为模型的输入进行多故障位置诊断，诊断平均准确率高于 70%，平均诊断时间小于一分钟。因此，所提出的模型的诊断精度和速度可以满足现场实际通风系统多故障位置诊断的工程要求，该模型可以有效地替代人员发现通风系统故障，也为智能通风系统的建设奠定了良好的基础。
