Application of fault propagation intensity in fault diagnosis of CNC machine tool

被引:3
作者
Zhang, Yingzhi [1 ]
Mu, Liming [1 ]
Liu, Jialin [1 ]
Liu, Jintong [1 ]
Tian, Zhifu [1 ]
Zhang, Yilong [1 ]
机构
[1] Jilin Univ, Sch Mech & Aerosp Engn, Dept Ind Engn, Changchun 130022, Jilin, Peoples R China
关键词
CNC machine tools; fault diagnosis; fault propagation intensity; fault propagation hierarchy model; PAGERANK; SEARCH;
D O I
10.1080/02533839.2019.1694439
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
To realize dynamic and real-time multi-fault decoupling and diagnostic CNC machine tools, this study proposes a dynamic fault diagnosis method that is based on fault propagation intensity. Integrated fault mechanism analysis, directed graph theory, and interpretative structure model are used to construct a fault propagation hierarchical model to visually depict complex fault causality. The influence degree of component nodes and the fault influence degree of edges are calculated using PageRank and a coupling degree function. The fault propagation probability of component nodes is determined by synthesizing node fault probability. Fault propagation intensity is defined by the probability of fault propagation and edge-betweenness to characterize the behavior of fault propagation dynamically. Combined with the hierarchical fault propagation model, the critical path and node are determined. A certain type of CNC machine tool is taken as an example to carry out a specific application. Results show that the hierarchical model of system fault propagation realizes multi-fault decoupling and clarifies the process of fault propagation. The critical path is identified according to the fault propagation intensity, the deviation caused by describing the behavior of fault propagation based on a single index is avoided, and the accuracy of fault diagnosis is improved.
引用
收藏
页码:153 / 161
页数:9
相关论文
共 30 条
[1]  
[Anonymous], 2017, T CHINA ELECTROTECH
[2]   Improving the analysis of dependable systems by mapping fault trees into Bayesian networks [J].
Bobbio, A ;
Portinale, L ;
Minichino, M ;
Ciancamerla, E .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2001, 71 (03) :249-260
[3]   The anatomy of a large-scale hypertextual Web search engine [J].
Brin, S ;
Page, L .
COMPUTER NETWORKS AND ISDN SYSTEMS, 1998, 30 (1-7) :107-117
[4]   A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks [J].
Duan, Rong-xing ;
Zhou, Hui-lin .
2012 INTERNATIONAL CONFERENCE ON FUTURE ELECTRICAL POWER AND ENERGY SYSTEM, PT B, 2012, 17 :1376-1382
[5]  
Gao JM, 2008, P REL MAINT S, P360
[6]  
Han Guang-chen, 2005, Computer Integrated Manufacturing Systems, V11, P794
[7]  
Hinz AM, 2012, BEHAV NEUROL, V25, P13, DOI [10.1155/2012/743238, 10.3233/BEN-2012-0345]
[8]  
Illingworth V., 1996, The Penguin Dictionary of Physics
[9]  
[贾学涵 Jia Xuehan], 2017, [电网技术, Power System Technology], V41, P1015
[10]   FAULT LOCATION USING DIGRAPH AND INVERSE DIRECTION SEARCH WITH APPLICATION [J].
KOKAWA, M ;
MIYAZAKI, S ;
SHINGAI, S .
AUTOMATICA, 1983, 19 (06) :729-735