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Application of WAVELET-SVM in Fault Diagnosis for the UV Control System

机译:小波-SVM在UV控制系统故障诊断中的应用

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Wavelet Analysis extracts the main feature from the fault signal through wavelet transformation, so it is advantageous to withdraw fault characteristic for fault diagnosis. Support Vector Machine (SVM) has shown its good classification performance in fault diagnosis. A new method of fault diagnosis for UV control system based on WAVELET-SVM is raised. The sensor output is sampled in frequency domain and it is preprocessed by wavelet to extract main vectors of the fault features. Fault patterns under various states are classified using multi-class SVM, and fault diagnosis is realized. The simulation results show that WAVELET-SVM is feasible to detect and locate faults quickly and exactly and has high robustness.
机译:小波分析通过小波变换从故障信号中提取主特征,因此有利于撤销故障诊断的故障特性。支持向量机(SVM)在故障诊断中显示了其良好的分类性能。提高了一种新的基于小波SVM的UV控制系统故障诊断方法。传感器输出在频域中采样,它被小波预处理以提取故障特征的主向量。使用多级SVM分类各种状态下的故障模式,实现故障诊断。仿真结果表明,小波-SVM可快速且精确地检测和定位故障并具有高稳健性。

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