Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher's criterion

Ziani Ridha; Felkaoui Ahmed; Zegadi Rabah

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Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher's criterion

机译：使用具有二元粒子群优化和正则化 Fisher 准则的多类支持向量机进行轴承故障诊断

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Condition monitoring of rotating machinery has attracted more and more attention in recent years in order to reduce the unnecessary breakdowns of components such as bearings and gears which suffer frequently from failures. Vibration based approaches are the most commonly used techniques to the condition monitoring tasks. In this paper, we propose a bearing fault detection scheme based on support vector machine as a classification method and binary particle swarm optimization algorithm (BPSO) based on maximal class separability as a feature selection method. In order to maximize the class separability, regularized Fisher's criterion is used as a fitness function in the proposed BPSO algorithm. This approach was evaluated using vibration data of bearing in healthy and faulty conditions. The experimental results demonstrate the effectiveness of the proposed method.

机译：近年来，旋转机械的状态监测越来越受到人们的关注，以减少轴承和齿轮等经常发生故障的部件的不必要故障。基于振动的方法是状态监测任务中最常用的技术。本文提出了一种基于支持向量机的轴承故障检测方案作为分类方法，提出了一种基于最大类分离性的二元粒子群优化算法（BPSO）作为特征选择方法。为了最大限度地提高类可分离性，在所提出的BPSO算法中，将正则化的Fisher准则用作适应度函数。使用轴承在健康和故障条件下的振动数据评估了这种方法。实验结果验证了所提方法的有效性。

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来源
《Journal of Intelligent Manufacturing》 |2017年第2期|405-417|共13页
作者
Ziani Ridha; Felkaoui Ahmed; Zegadi Rabah;
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作者单位

Ferhat Abbes Univ Setif 1, Inst Opt & Precis Mech, Lab Appl Precis Mech, Setif 19000, Algeria;

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正文语种英语
中图分类自动化系统;
关键词
Support vector machines (SVMs); Particle swarm optimization (PSO); Regularized linear discriminant analysis (RLDA); Features selection; Condition monitoring;

机译：支持向量机（SVM）;粒子群优化（PSO）;正则化线性判别分析（RLDA）;功能选择;状态监测;

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1. Research from Dalian Jiaotong University Has Provided New Data on Support Vector Machines (Research on Rolling Bearing Fault Diagnosis Based on Variational Modal Decomposition Parameter Optimization and an Improved Support Vector Machine) [J] . Robotics & Machine Learning Daily News . 2023,第29期

机译：Research from Dalian Jiaotong University Has Provided New Data on Support Vector Machines (Research on Rolling Bearing Fault Diagnosis Based on Variational Modal Decomposition Parameter Optimization and an Improved Support Vector Machine)
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机译：Research Reports on Support Vector Machines from College of Aeronautical Engineering Provide New Insights (Bearing fault diagnosis model using improved Bayesian information criterion-based variational modal decomposition and IGA-SVM)
3. Diagnosis of ball-bearing faults using support vector machine based on the artificial fish-swarm algorithm [J] . Chih-Jer Lin, Wen-Lin Chu, Cheng-Chi WangChih-Keng ChenI-Ting Chen Journal of low frequency noise, vibration and active control . 2020,第4期

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机译：基于优化遗传神经网络的井下运输机械滚动轴承故障诊断（The Application of Optimizing the GENETIC NEURAL NETWORK to the Fault Diagnosis of Rolling Bearings of Transporting Machinery Underground）

Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher's criterion

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