School of Mechanical and Vehicle Engineering, Taiyuan University of Technology
Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment

针对液压支架故障隐蔽性强,大量历史检测数据未得到有效分析与挖掘的现状,提出一种基于融合卷积transformer的液压支架故障诊断方法,该方法可以同时发挥CNN提取局部特征和transformer识别全局信息的优势,提取到隐藏在数据中的更多有用特征,以实现对液压支架故障的诊断。利用山西斜沟煤矿液压支架历史数据对所提方法的诊断性能进行实验验证。结果表明,与WDCNN模型、transformer模型以及BiLSTM相比,所提方法对液压支架故障具有良好的诊断效果,其故障诊断准确率达到了99.53%。为液压支架故障的确定提供理论依据,具有一定的工程应用价值。

In view of the high fault concealment of hydraulic support and the lack of effective analysis and mining of a large amount of historical detection data, a fault diagnosis method of hydraulic support based on Fusion CNN Transformer(FCT) is proposed. This method can give full play to the advantages of CNN’s extraction of local features and transformer’s recognition of global information, and extract more useful features hidden in the data to realize fault diagnosis of hydraulic support. The diagnostic performance of the proposed method is verified by experiments with the historical data of hydraulic support in Shanxi Xiegou Coal Mine. The results show that, compared with WDCNN model, transformer model and BiLSTM, the proposed method has a good fault diagnosis effect for hydraulic support, and the fault diagnosis accuracy rate reaches 99. 53% . It provides a theoretical basis for determining the fault of hydraulic support and has certain engineering application value.

山西省科技重大专项计划“揭榜挂帅”项目(202101010101018);山西省科技合作交流专项项目(202104041101003);山西省科技创新人才团队(202204051001017)

马子越,丁华,廉自生,等.基于融合卷积Transformer的液压支架故障诊断方法[J].煤炭工程,2025,57(1):137-143.