Luo Wen, Zhao Youxin
China Energy Shendong Coal Group Co., Ltd, Yulin, 719315, Shanxi, China.
CCRI Tongan (Beijing) Intelligent Control Technology Co., Ltd, Beijing, 100013, China.
Sci Rep. 2024 Nov 6;14(1):27009. doi: 10.1038/s41598-024-73527-0.
To address the problem of fault branch recognition in mine ventilation systems, a one-class classification algorithm is introduced to construct the MC-OCSVM ventilation system fault diagnosis model, which is integrated with multiple OCSVMs. This model adopts uniform hyperparameters and transforms the ventilation system fault diagnosis problem into a maximum decision distance problem, to realize the effective use of mine monitoring wind speed data. The experiments on public KEEL datasets verify that the one-class classification integration model can solve the multiclassification problem and that the MC-OCSVM model has better generalizability than other one-class classification integration models do. The experiment is carried out in the Buertai coal mine, and the results show that the proposed algorithm can identify fault branches quickly and accurately, with an accuracy of 93.2% and a single fault diagnosis time is 1.2 s, highlighting its strong robustness.
为解决矿井通风系统中故障分支识别的问题，引入一类分类算法构建了与多个单类支持向量机（OCSVM）集成的MC-OCSVM通风系统故障诊断模型。该模型采用统一的超参数，将通风系统故障诊断问题转化为最大决策距离问题，以实现对矿井监测风速数据的有效利用。在公共KEEL数据集上的实验验证了一类分类集成模型能够解决多分类问题，且MC-OCSVM模型比其他一类分类集成模型具有更好的泛化能力。在布尔台煤矿进行的实验结果表明，所提算法能够快速、准确地识别故障分支，准确率为93.2%，单次故障诊断时间为1.2秒，突出了其强大的鲁棒性。
