数学季刊 >

2026 , Vol. 41 >Issue 2: 197 - 206

DOI: https://doi.org/10.13371/j.cnki.chin.q.j.m.2026.02.008

基于 BA-SVM 梯形区域极点分类的故障诊断

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  • 1. School of Mathematics and Systematic Sciences, Shenyang Normal University, Shenyang 110034, China; 2. Department of Basic Education, Shenyang Institute of Engineering, Shenyang 110136, China
DU Zi-wei (1998-), female, native of Zhoukou, Henan, master student of Shenyang Normal University, engages in fault diagnosis and reliable control research; YAO Bo (1963-), female, professor of Shenyang Normal University, Master supervisor, engages in fault diagnosis and reliable control and other aspects of research; WANG Fu-zhong (1963-), male, professor of Shenyang Institute Of Engineering, Master supervisor, engages in fault diagnosis and reliable control and other aspects of research. 
YAO Bo (1963-), female, professor of Shenyang Normal University, Master supervisor, engages in fault diagnosis and reliable control and other aspects of research;

收稿日期: 2022-08-31

  网络出版日期: 2026-06-30

基金资助

Supported by the National Natural Science Foundation of China (Grant No. 12101417).

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Fault Diagnosis Based on BA-SVM Trapezoidal Region Pole Classification

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  • 1. School of Mathematics and Systematic Sciences, Shenyang Normal University, Shenyang 110034, China; 2. Department of Basic Education, Shenyang Institute of Engineering, Shenyang 110136, China
DU Zi-wei (1998-), female, native of Zhoukou, Henan, master student of Shenyang Normal University, engages in fault diagnosis and reliable control research; YAO Bo (1963-), female, professor of Shenyang Normal University, Master supervisor, engages in fault diagnosis and reliable control and other aspects of research; WANG Fu-zhong (1963-), male, professor of Shenyang Institute Of Engineering, Master supervisor, engages in fault diagnosis and reliable control and other aspects of research. 
YAO Bo (1963-), female, professor of Shenyang Normal University, Master supervisor, engages in fault diagnosis and reliable control and other aspects of research;

Received date: 2022-08-31

  Online published: 2026-06-30

Supported by

Supported by the National Natural Science Foundation of China (Grant No. 12101417).

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摘要

For a class of linear constant systems, the problem of continuous gain type fault diagnosis and reliable control of a single component of an actuator is investigated based on the trapezoidal region. Firstly, in order to solve the problem that the pole information of closed-loop system is difficult to observe, a design scheme of full-dimensional state observer is given to realize the real-time observation of pole information and form a pole classification database for system failure. Secondly, according to the characteristics that the poles are located in different areas when different channels have faults, support vector machine is applied to design a pole classifier to diagnose faults in the system and
achieve accurate and reliable control of the system based on the fault diagnosis results. Then, in order to solve the problem of difficulty in selecting parameters for support vector machines, the bat algorithm (BA) is proposed to achieve automatic parameter optimization, which has the advantages of strong robustness and easy to combine with
other methods.

关键词： Pole classifier; Support vector machine; Bat algorithm; Regional pole assignment; Fault diagnosis

本文引用格式

杜紫薇, 姚波, 王福忠 . 基于 BA-SVM 梯形区域极点分类的故障诊断[J]. 数学季刊, 2026 , 41(2) : 197 -206 . DOI: 10.13371/j.cnki.chin.q.j.m.2026.02.008

Abstract

For a class of linear constant systems, the problem of continuous gain type fault diagnosis and reliable control of a single component of an actuator is investigated based on the trapezoidal region. Firstly, in order to solve the problem that the pole information of closed-loop system is difficult to observe, a design scheme of full-dimensional state observer is given to realize the real-time observation of pole information and form a pole classification database for system failure. Secondly, according to the characteristics that the poles are located in different areas when different channels have faults, support vector machine is applied to design a pole classifier to diagnose faults in the system and
achieve accurate and reliable control of the system based on the fault diagnosis results. Then, in order to solve the problem of difficulty in selecting parameters for support vector machines, the bat algorithm (BA) is proposed to achieve automatic parameter optimization, which has the advantages of strong robustness and easy to combine with
other methods.

Key words： Pole classifier; Support vector machine; Bat algorithm; Regional pole assignment; Fault diagnosis

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