基于SVM的液压机械驱动齿轮组故障诊断研究

Research on fault diagnosis of hydraulic mechanical drive gear set based on SVM

针对液压机械驱动齿轮组故障诊断结果精准度不佳、可靠性差等问题,本文提出基于SVM的液压机械驱动齿轮组故障诊断研究。采集了液压机械驱动齿轮组振动信号,构建液压机械驱动齿轮组故障信号分离模型;运用低秩算法分离液压机械驱动齿轮箱振源信号,设计齿轮组故障信号约束条件,完成液压机械驱动齿轮组分类;根据分类结果,采用SDAE模型提取液压机械驱动齿轮组故障特征,并将提取结果输入到支持向量机内训练,其最终输出结果就是最佳诊断结果,实现基于SVM的液压机械驱动齿轮组故障诊断研究。实验结果表明,通过对该方法开展故障检测及故障诊断测试,本文方法下分类错误率不超过3.5%,验证了该方法的可行性高。

This paper addresses the challenges of poor accuracy and reliability in fault diagnosis for hydraulic mechanical drive gear sets by proposing a research approach based on Support Vector Machines(SVM).The study begins by collecting vibration signals from the hydraulic mechanical drive gear group and constructing a fault signal separation model.Utilizing a low-rank algorithm,the research separates the vibration source signals of the hydraulic mechanical drive gearbox.Constraint conditions are designed for gear group fault signals to facilitate their classification.Based on these classification results,the SDAE model is employed to extract fault features from the hydraulic mechanical drive gear group.The extracted features are then input into the SVM for training,with the final output being the optimal diagnostic result.This approach achieves fault diagnosis of the hydraulic mechanical drive gear group based on SVM.Experimental results demonstrate that the classification error rate of this method does not exceed 3.5%,confirming its high feasibility.