基于PSO-SVM的齿轮箱故障诊断研究

Research of Gear Box Fault Diagnosis Based on PSO-SVM

针对目前齿轮箱故障诊断存在的检测难度大、主观性强、准确性不高等问题,提出了一种基于粒子群算法和支持向量机的故障诊断方法。运用时域频域分析法对振动信号进行分析获取特征值,利用支持向量机(SVM)技术对齿轮箱特征参数进行模式识别和故障分类,并引入粒子群算法(PSO)用于优化支持向量机参数,建立了齿轮箱典型故障诊断模型。实验结果表明:该方法可以对齿轮箱不同故障类型进行准确的分类,有效的提高了齿轮箱故障诊断的可靠性。

Aiming at the problems existing in gear box fault diagnosis such that difficult to detecting,strong subjectivity and low accuracy,a fault diagnosis method based on particle swarm optimization（ PSO） algorithm and support vector machine（ SVM） is proposed. In this method the time domain analysis and frequency domain analysis are conducted to get the characteristic value of vibration signals,and the SVM is used for pattern recognition and fault classification of the characteristic parameters of gearbox,and PSO is introduced in the optimization of SVM parameters. A typical gearbox fault diagnosis model is established and the experimental results show that the method can classify different gear box fault type accurately and the reliability of the gear box fault diagnosis is effectively improved.