Multi-dimensional Fault Diagnosis Method Based on Expert Thinking

doi:10.3901/JME.2017.23.032

Abstract

Abstract: In view of the low diagnosis accuracy of expert system at present, a multi-dimensional fault diagnosis method based on expert thinking is proposed, which fuse information of related points, multi-times and multi-sensitive parameters. Following the thinking when fault diagnosis expert analyze and diagnose the machine's abnormal state and base on the failure mechanism, response characteristics and deterioration law, select the data of related-points in multi-times to establish the matrix of sensitive parameters, then generate the fault matrix and weight matrix to diagnose the machine's abnormal state. The result is accurate and conflicts between different times can be avoided when using this method to analyze and diagnose the abnormal state of a machine in real petrochemical.

Key words: eight matrix, expert thinking, fault diagnosis, fault matrix, sensitive parameter matrix

CLC Number:

TP277

MA Bo, GAO Jinji, JIANG Zhinong. Multi-dimensional Fault Diagnosis Method Based on Expert Thinking[J]. Journal of Mechanical Engineering, 2017, 53(23): 32-38.

References

[1] SHENG Chenxing, LI Zhixiong, LI Qin, et al. Recent progress on mechanical condition monitoring and fault diagnosis[J]. Procedia Engineering, 2011(15):142-146.
[2] NIU G, YANG B S. Intelligent condition monitoring and prognostics system based on data-fusion strategy[J].Expert Systems with Applications, 2010, 37(12):8831-8840.
[3] NIU Gang, HAN Tian, YANG B S, et al. Multi-agent decision fusion for motor fault diagnosis[J]. Mechanical Systems and Signal Processing, 2007(21):1285-1299.
[4] ANTON, VITKO, LADISLAV, et al. Navigation and diagnosis of walking robot[J]. Mechanical Engineering and Automation, 2012, 54(9):568-572.
[5] OZYURT B, KANDEL A. A hybrid hierarchical neural network-fuzzy expert system approach to chemical process fault diagnosis[J]. Fuzzy Sets and Systems, 1996(83):11-25.
[6] QIAN Yu, XU Liang, LI Xinxi, et al. LUBRES:An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process[J]. Expert System with Application, 2008(35):1252-1266.
[7] NAN C, KHAN F, IQBAL M T. Real-time fault diagnosis using knowledge-based expert system[J]. Process Safety and Environmental Protection, 2008, 86:33-71.
[8] 吴蒙,贡壁,何振亚. 基于并行联想记忆的机械故障诊断专家系统[J]. 振动、测试与诊断, 1992, 12(2):1-5. WU Meng, GONG Bi, HE Zhenya. Mechanical fault diagnosis expert system based on parallel associative memory[J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration Measurement & Diagnosis, 1992, 12(2):1-5.
[9] 高金吉. 基于矩阵判别筛选法的机械故障诊断专家系统[J]. 振动、测试与诊断, 1998, 18(2):120-124. GAO Jinji. A mechanical fault diagnosis expert system based on matrix distinguishing sieve method[J]. Journal of Vibration Measurement & Diagnosis, 1998, 18(2):120-124.
[10] 姚剑飞,江志农,赵庆亮,等. 基于故障原因-征兆矩阵的故障诊断专家系统[J]. 振动、测试与诊断, 2009, 29(1):74-79. YAO Jianfei, JIANG Zhinong, ZHAO Qingliang. A fault diagnosis expert system based on fault cause-symptom matrix[J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration Measurement & Diagnosis, 2009, 29(1):74-78.
[11] 刘贵立,王立鹏,张国英. 基于Alopex神经网络的旋转机械故障诊断专家系统[J]. 振动、测试与诊断, 2004, 24(1):61-65. LIU Guili, WANG Lipeng, ZHANG Guoying. Research on horizontal vibration of 32 m span simply supported railroad bridges[J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration Measurement & Diagnosis, 2004, 24(1):61-65.
[12] 李萍. 基于多振动信号信息融合的旋转机械故障诊断方法[D]. 湘潭:湖南科技大学, 2008. LI Ping. The information fusion of rotating machinery fault diagnosis based on multi-vibration signal[D]. Xiangtan:Hunan University of Science and Technology, 2008.
[13] 谭青, 向阳辉. 加权证据理论信息融合方法在故障诊断中的应用[J]. 振动与冲击, 2008, 27(4):112-116. TAN Qing, XIANG Yanghui. Application of weighted evidential theory and its information fusion method in fault diagnosis[J]. Journal of Vibration & Shock, 2008, 27(4):112-116.
[14] 赵世荣, 黄向华. 应用神经网络信息融合诊断航空发动机故障[J]. 航空动力学报, 2008, 23(1):163-168. ZHAO Shirong, HUANG Xianghua. Fault diagnosis for aeroengine gas path components based on neural network multisensor data fusion[J]. Journal of Aerospace Power, 2008, 23(1):163-168.
[15] TRIBENI P B, SWAGATAM D. Multi-sensor data fusion using support vector machine for motor fault detection[J]. Information Science, 2012, 217:96-107.
[16] 原新,朱齐丹,兰海. 基于粗糙集理论的多传感器信息融合[J]. 哈尔滨工业大学学报, 2006, 38(10):1669-1673. YUAN Xin, ZHU Qidan, LAN Hai. Multi-sensor information fusion based on rough set theory[J]. Journal of Harbin Institute of Technology, 2006, 38(10):1669-1672.
[17] 项春. 离心式压缩机旋转失速的故障机理研究[J]. 振动、测试与诊断, 2002, 22(1):54-60. XIANG Chun. Fault mechanism of rotating stall of centrifugal compressor[J]. Journal of Vibration Measurement & Diagnosis, 2002, 22(1):54-60.
[18] 王洋,夏松波,高金吉. DH型离心空压机旋转失速故障诊断及治理[J]. 振动工程学报, 2000, 13(3):487-492. WANG Yang, XIA Songbo, GAO Jinji. Fault diagnosis and treatment of rotating stall for DH type centrifugal compressor[J]. Journal of Vibration Engineering, 2000, 13(3):487-492.
[19] NICOAL G, STEPHANE B, FRANCIS L, et al. Simulation of rotating stall in a whole stage of an axial compressor[J]. Computers & Fluids, 2010(39):1644-1655.
[20] 晋风华. 基于故障矢量的汽轮机叶片脱落故障诊断方法研究[J]. 燃气轮机技术, 2007, 20(3):35-37. JIN Fenghua. Research on the methods of fault orientation of blade sheding based on fault vector for steam turbine[J]. Gas Turbine Technology, 2007, 20(3):35-37.