Research on intelligent diagnosis system of control card based on GABP neural network

被引:0
作者
Chai, Wei [1 ,2 ]
Guo, Jiang [1 ,2 ]
Zhang, Kefei [1 ,2 ]
Meng, Ke [1 ,2 ]
Wu, Hongyan [1 ,2 ]
机构
[1] Wuhan Univ, Sch Power & Mech Engn, Wuhan, Hubei, Peoples R China
[2] Wuhan Univ, Minist Educ Hubei Prov, Key Lab Hydraul Machinery Transients, Wuhan, Hubei, Peoples R China
来源
PROCEEDINGS OF 2017 IEEE 2ND INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC) | 2017年
关键词
Electric system; Control card; Neural network; Intelligent diagnosis; Information System;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The digital level of power station has been improved, and the control card used to maintain the normal operation of the electrical system has been produced. However, there are few studies on the maintenance and diagnosis of the card, it has a waste of cards and requires high-level maintenance. In this paper, this kind of control card is taken as the research object. With the help of the neural network model, a systematic intelligent diagnosis model is formed. On this basis, the intelligent diagnosis system is developed to realize automatic power-on test, automatic data acquisition and transmission. Through the analysis of upper machine processing directly diagnosis, reduce the workload of maintenance personnel, improve the value of test data, and promote the electric system intensive management in the field of equipment maintenance.
引用
收藏
页码:1249 / 1253
页数:5
相关论文
共 9 条
  • [1] Cai M, 2010, ATOMIC ENERGY SCI TE
  • [2] Ding X. T., 2011, RES FAULT DIAGNOSIS
  • [3] Wang A. L., 2014, J SICHUAN ORDNANCE, P42
  • [4] Wang J S, 2014, SCI WORLD J, V2014
  • [5] Xiang Shaobing, 2014, ELECT PRODUCT RELIAB, V04, P22
  • [6] Xue Han, 2010, Computer Measurement & Control, V18, P8
  • [7] Zhang D. H., 2013, NW HYDROPOWER, P66
  • [8] Zhou G., 2008, ATOMIC ENERGY SCI S, V42, P92
  • [9] Zi-Lin S U, 2011, LUDONG U J