主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2021, Vol. 40 ›› Issue (2): 263-267.

• • 上一篇    下一篇

基于IPSO-DBN 的管道故障诊断方法

王新颖1，赵斌1，张瑞程1，黄旭安1，陈海群2   

  1. 1. 常州大学环境与安全工程学院，江苏常州213164；2. 常州大学石油化工学院，江苏常州213164
  • 出版日期:2021-02-15 发布日期:2021-02-15
  • 通讯作者: 陈海群（1971-），男，常州大学石油化工学院教授，博士。
  • 作者简介:王新颖（1976-），女，黑龙江海伦人，常州大学环境与安全工程学院副教授，硕士，主要从事安全检测、监控研究，江苏省常州市武进区滆湖中路21 号，213164。
  • 基金资助:

    江苏省研究生科研与实践创新计划资助项目(KYCX20- 2590)；常州市科技项目“城市地下燃气管网信息化管理与应急决策支持系统”(CZ20170017)

Pipeline fault diagnosis method based on IPSO-DBN

WANG Xin-ying1,ZHAO Bin1, ZHANG Rui-cheng1,HUANG Xu-an1, CHEN Hai-qun2   

  1. 1. School of Environmental and Safety Engineering, Changzhou University, Jiangsu Changzhou 213164, China; 2. Petrochemical School, Changzhou University, Jiangsu Changzhou 213164, China
  • Online:2021-02-15 Published:2021-02-15

摘要：

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。

关键词: 燃气管道, 故障诊断, 粒子群算法, 深度信念网络

Abstract: 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. 

Key words: gas pipeline, fault diagnosis, particle swarm optimization, deep belief network