Research on the fault diagnosis method for high-speed loom using rough set and Bayesian network

被引：4

作者：

Xiao, Yanjun ^{[1
]}

Zhang, Heng ^{[1
]}

Zhou, Wei ^{[1
]}

Wan, Feng ^{[1
]}

Meng, Zhaozong ^{[1
]}

机构：

[1] Hebei Univ Technol, Sch Mech Engn, Dept Measurement & Control, Tianjin, Peoples R China

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2020年 / 39卷 / 01期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

High-speed loom; fault diagnosis; rough set theory; Bayesian network; SAFETY;

D O I：

10.3233/JIFS-192039

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The textile industry has a long history and a large market scale around the world. High-speed loom belongs to the high-end production equipment of the textile industry with the characteristics of high precision, high speed and high efficiency. However, due to its expensive cost and complex structure, there might be significant loss once a high-speed loom breaks down. At present, the monitoring and troubleshooting of high-speed loom operation mainly depend on the experience of maintenance people to carry out inspections, which is inefficient, time-consuming, laborious and less efficient. In this paper, a fault diagnosis method for high-speed loom based on rough set and Bayesian network is investigated. Rough set theory is applied to reduce the attributes of fault causes and results and find the minimum reduction and classification rules. Then, a Bayesian fault diagnosis network model is built, and the probability of each fault cause is calculated to find the maximum probability. Finally, the diagnosis results are obtained. The experimental results have demonstrated the reliability and convenience of the faults diagnosis method for the high-speed loom.

引用

页码：1147 / 1161

页数：15

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