Intelligent fault detection and analysis based on support vector machine and applications to Aeroengine

被引：1
作者：
Ren, Hongquan [1 ]
Fan, Quan-Yong [1 ]
Song, Xuekui [2 ]
Li, Hongxia [3 ]
机构：
[1] Northwestern Polytech Univ, Sch Automat, Xian 710129, Shaanxi, Peoples R China
[2] Ansteel Engn Technol Corp Ltd, Anshan 114021, Liaoning, Peoples R China
[3] North Automat Control Technol Inst, Taiyuan 030006, Shanxi, Peoples R China
来源：
2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2022年
关键词：
Support vector machine(SVM); Fault detection; Intelligent classification; SVM-RFE; SELECTION; CLASSIFICATION; INVERTIBILITY; SYSTEMS;
D O I：
10.1109/CCDC55256.2022.10033535
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the problem of intelligent fault detection for switched systems is investigated based on SVM. In order to illustrate the ability of SVM in distinguishing normal and faulty systems, a model-based analysis method is introduced in this paper. Different from the existing methods, this paper considers the influence of disturbance on the output data. The theoretical model of SVM is given, which can realize the fuzzy distinction between normal and fault system data, and describe the indistinguishable region affected by disturbance. Finally, one numerical model of aeroengine is used to illustrate the effectiveness of the proposed analysis.
引用
收藏
页码：2680 / 2685
页数：6
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