基于IWOA-SVM的电路软故障诊断

Circuit soft fault diagnosis based on IWOA-SVM

针对DC-DC电路软故障诊断准确度不高的问题,提出了一种基于改进鲸鱼(IWOA)优化支持向量机(SVM)的电路软故障诊断方法。首先,对故障信号进行VMD提取特征向量;然后通过引用反馈机制来改善传统鲸鱼算法的全局搜索能力防止陷入局部最优,把线性因子改为非线性因子用来平衡全局搜索和局部开发能力来改进鲸鱼算法,以解决易陷入局部最优和局部开发能力低的问题。最后建立IWOA-SVM模型用来进行电路软故障诊断,最终对电路软故障诊断准确度不高的问题,实现了高效的诊断。根据故障诊断的结果表明,改进后的鲸鱼算法优化支持向量机相比本文对比的其他方法具有更好的诊断效果。故障识别准确率达到了99.166 7%。

A circuit soft fault diagnosis method based on improved whale(IWOA) optimized support vector machine(SVM) is proposed to address the problem of DC-DC circuit soft fault diagnosis with low accuracy. First, a VMD is performed on the fault signal to extract the feature vectors. Then we improve the global search ability of the traditional whale algorithm to prevent falling into local optimum by referring to the feedback mechanism, and change the linear factor to a nonlinear factor to balance the global search and local exploitation ability to improve the whale algorithm to solve the problem of easily falling into local optimum and low local exploitation ability. Finally, the IWOA-SVM model is used for soft fault diagnosis of circuits, and finally, the problem of low accuracy of soft fault diagnosis of circuits is achieved with high efficiency. Based on the results of fault diagnosis, it is shown that the improved whale algorithm optimized support vector machine has better diagnosis effect compared with other methods compared in this paper. Fault recognition accuracy of 99.166 7%.