A new model of mine hoist fault diagnosis based on the rough set theory

被引：2

作者：

Xia Zhanguo ^{[1
]}

Wang Zhixiao ^{[1
]}

Wang Ke ^{[1
]}

Guan Hongjie ^{[1
]}

机构：

[1] China Univ Min & Technol, Sch Comp Sci & Technol, Beijing, Peoples R China

来源：

PROCEEDINGS OF NINTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING | 2008年

关键词：

D O I：

10.1109/SNPD.2008.85

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Extraction of simple and effective rules for fault diagnosis is one of the most important issues needed to be addressed in fault diagnosis, because available information is often inconsistent and redundant. This paper presents a fault diagnosis model based on rough set theory. Firstly, this model can discretize fault continued attributes using a modified genetic algorithm. Then, reduce diagnosis rule by using heuristic algorithm of rough set theory, a set of diagnosis rules are generated and a rule database for fault diagnosis is established. Simulation results for fault diagnosis of mine hoist show that this method improves the accuracy rate of fault diagnosis, predigest the number of feature parameters and diagnostic rules, and reduces the cost of diagnosis, with more applicable than the classical RS-method in practical applications.

引用

页码：649 / 654

页数：6

共 50 条

[31] Fault Diagnosis of Mine Hoist Brake System Based on Improved SAE
Li J.
Yan F.
Miao D.
Liu Y.
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 2022, 42 (09): : 928 - 934
[32] Intelligent fault diagnosis method of mine hoist based on knowledge engineering
Li J.-L.
Yang Z.-J.
Pang X.-Y.
1600, China Coal Society (41): : 1309 - 1315
[33] Fault detection and diagnosis of mine hoist machine based on RS and SVM
Ma, Xianmin
Zhang, Xing
Yang, Zhanshe
2016 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C), 2016, : 531 - 535
[34] Prediction and Diagnosis of Mine Hoist Fault Based on Wavelet Neural Network
Zhu Xijun
Guo Jinyun
Wei Chongyu
2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11, 2008, : 598 - +
[35] A New Fault Diagnosis Model of Electric Power Grid Based on Rough Set and Neural Network
Zhang Liying
Wang Dazhi
Zhang Cuiling
Liu Xiaoqin
2012 FOURTH INTERNATIONAL CONFERENCE ON MULTIMEDIA INFORMATION NETWORKING AND SECURITY (MINES 2012), 2012, : 405 - 408
[36] Decision Network Model for Vibration Fault Diagnosis of Steam Turbine-generator Set Based on Rough Set Theory
Zhang Aiping
Cao Liming
Yang Yang
He Xiangying
2009 INTERNATIONAL CONFERENCE ON SUSTAINABLE POWER GENERATION AND SUPPLY, VOLS 1-4, 2009, : 2245 - +
[37] Rough set theory based fault diagnosis of node in wireless sensor network
School of Automatic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Beijing Youdian Daxue Xuebao, 2007, 4 (69-73):
[38] Modeling and Fault Diagnosis for Coupled-tank Based on Rough Set Theory
Yun, Gao
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 5702 - 5707
[39] Research on fault diagnosis of FPC based on virtual instrument and rough set theory
Li, Yun-fei
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 3891 - 3895
[40] Improved fault diagnosis algorithm based on artificial immune network model and neighbourhood rough set theory
Zheng, Yonghuang
Li, Benhong
Zhang, Shangmin
COGNITIVE COMPUTATION AND SYSTEMS, 2021, 3 (04) : 323 - 331

← 1 2 3 4 5 →