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。
In order to accurately extract the fault frequency characteristics of the motor signal, a fault harmonic detection method based on differential analysis and wavelet threshold is proposed. The difference analysis solves the interference problem of the fundamental frequency to the fault frequency. The results show that the noise in the signal has a great influence on the detection of the fault frequency, and the wavelet threshold function has a good noise re- duction capability. Combined with the advantages between the two, the differential analysis method was to eliminate the fundamental frequency, and then the improved threshold function was used to eliminate noise in the processing of difference signals. The simulation results show that the proposed method can improve the detection performance of the fault frequency. Compared with the traditional detection method, the fault frequency feature can be extracted more easily.
作者
苑亚南
朱希安
张涛
YUAN Ya nan ZHU Xi - an ZHANG Tao(College of Information and Communication Engineering, Beijing Information Science and Technology University,Beijing 100101 ,China)
出处
《计算机仿真》
北大核心
2017年第10期401-405,共5页
Computer Simulation
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)
关键词
信号故障检测
小波阈值去噪
差量分析
阈值函数
Signal fault detection
Wavelet threshold denoising
Difference analysis
Threshold function
