基于PSO-BP复合网络的掘进机截割部故障智能诊断  

Intelligent fault diagnosis of cutting part of tunnel boring machine based on PSO-BP composite network

作　　者：张世丽 ZHANG Shili(Changcun Coal Mine,Lu’an Chemical Group Co.,Ltd.,Changzhi 046000,China)

机构地区：[1]潞安化工集团常村煤矿,山西长治046000

出　　处：《陕西煤炭》2024年第6期128-132,共5页Shaanxi Coal

摘　　要：针对井下掘进机故障诊断频发,传统诊断方法和BP神经网络诊断周期长的情况,以常村煤矿矿用EBZ-160TY型掘进机为背景,提出基于PSO-BP神经网络模型的掘进机截割部智能诊断模型。该模型能够弥补BP神经网络模型收敛周期长、局部最优搜索差的缺点,实现模型的快速收敛和故障准确预测。通过设置PSO-BP神经网络模型参数、样本数据训练,同时经过数据测试,确定PSO-BP神经网络模型预测结果故障预测率为100%,而BP神经网络的预测精度为80%,且在同时间下,PSO-BP神经网络较BP神经网络预测精度更高。在同精度下,PSO-BP神经网络模型收敛速度更快,在精度为1×10^(-5)时,PSO-BP神经网络模型仅需7步,BP神经网络平均需要198.5步。综合测试结果说明,PSO-BP神经网络模型能够较快实现掘进机故障的预测,且达到较高的预测精度,为掘进机故障诊断提供依据。In view of the frequent fault diagnosis of underground tunnel boring machine(TBM)and the long diagnosis period of traditional diagnosis methods and BP neural network,taking EBZ-160TY TBM in Changcun Coal Mine as the background,an intelligent diagnosis model of TBM cutting part based on PSO-BP neural network model is proposed,which can overcome the long convergence period of BP neural network model and realize the fast convergence of the model and the accurate prediction of fault.By setting PSO-BP neural network model parameters,sample data training,and data testing at the same time,it is determined that the fault prediction rate of the PSO-BP neural network model is 100%,while the prediction accuracy of BP neural network is 80%.Moreover,at the same time,the prediction accuracy of PSO-BP neural network is higher than that of BP neural network.Under the same accuracy,the convergence speed of PSO-BP neural network model is faster.When the accuracy is 1×10^(-5),the PSO-BP neural network model only needs 7 steps,while the average BP neural network needs 198.5 steps.The comprehensive test results show that the PSO-BP neural network model can realize the faster prediction of fault for TBM with higher accuracy,which provides a basis for fault diagnosis of TBM.

关 键 词：掘进机截割部 PSO-BP神经网络模型 故障智能诊断 数据样本 收敛速度 预测精度 

分 类 号：TD421[矿业工程—矿山机电]