电机噪声故障信号优化检测仿真研究

发布时间：2018-03-30 15:59

本文选题：信号故障检测　切入点：小波阈值去噪　出处：《计算机仿真》2017年10期

【摘要】：为了准确提取电机信号故障频率特征,提出了一种基于差量分析和小波阈值的故障谐波检测方法。差量分析解决了基波频率对故障频率的干扰问题。研究表明信号中的噪声会对故障频率的检测产生较大影响,小波阈值函数具有很好的消噪能力。结合两者之间的优点,先利用差量分析法对基波频率进行消除,再将处理后的差量信号利用改进阈值函数消除噪声。仿真结果表明,所提的方法提高了故障频率的检测性能。与传统的检测方法相比较,故障频率特征更易提取。
[Abstract]:In order to accurately extract the motor fault signal frequency characteristics, this paper presents a fault detection method of harmonic differential analysis based on wavelet threshold and solves the interference problem. The fault frequency of the fundamental frequency differential analysis. The results show that the noise in the signal will have a greater impact on the frequency of fault detection, wavelet threshold denoising function with ability good. Combined with the advantages of both, the first use of the fundamental frequency of the elimination of differential analysis method, and then after the differential signal using the improved threshold function to remove noise. The simulation results show that the proposed method improves the detection performance of fault frequency. Compared with the traditional detection methods, the fault frequency characteristic easy to extract.

【作者单位】：北京信息科技大学信息与通信工程学院;
【基金】：国家自然基金面上项目(51374223) 北京市科技提升计划项目(PXM2016_014224_000021)
【分类号】：TM307

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