基于IPSO-DBN的管道故障诊断方法

针对城市燃气管道故障诊断效果不佳的问题,提出了一种基于改进粒子群算法优化深度信念网络(IPSO-DBN)的管道故障诊断方法。该方法首先对粒子群算法(PSO)中的惯性权重ω、加速因子C1和C2进行修正,得到改进粒子群优化算法(IPSO),并采用两种基准函数对比测试PSO与IPSO的网络性能,证明所选改进方法的优越性。其次利用IPSO优化深度信念网络(DBN)的初始权重,建立合适的DBN网络,将4种不同燃气管道工况下的实验数据用于IPSO-DBN网络训练及预测。最后将实验所得的故障诊断准确率与BP、DBN、PSO-DBN方法进行对比分析。实验结果表明,对于燃气管道不同工况下的故障分类识别,IPSO-DBN方法的平均测试集诊断准确率高达94.5%,诊断效果优于传统的BP、DBN以及PSO-DBN方法。

Pipeline fault diagnosis method based on IPSO-DBN

Aiming at the problem of poor performance of urban gas pipeline fault diagnosis, a pipeline fault diagnosis method based on improved particle swarm optimization optimized deep belief network (IPSO-DBN) is proposed. This method first modifies the inertia weight ω, acceleration factor C1 and C2 in the particle swarm optimization algorithm (PSO) to obtain an improved particle swarm optimization algorithm (IPSO), and uses two benchmark functions to compare and test the network performance of PSO and IPSO to prove the superiority of the selected improvement method. Secondly, use IPSO to optimize the initial weights of the deep belief network (DBN), establish a suitable DBN network, and use the experimental data under four different gas pipeline conditions for training and prediction of the IPSO-DBN network. Finally, the fault diagnosis accuracy obtained from the experiment is compared and analyzed with BP, DBN, PSO-DBN methods. Experimental results show that for the fault classification and identification of gas pipelines under different working conditions, the average test set diagnosis accuracy of the IPSO-DBN method is as high as 94.5%, and the diagnosis effect is better than the traditional BP, DBN and PSODBN methods.