摘要
振动信号是最能够全面反映电机运行状态的信号,所以在相关研究中一般都通过分析振动信号提取状态特征.振动信号属于非平稳随机信号.信号的奇异性部分往往包含了非常重要的信息,因此奇异性检测成为振动信号处理的主要内容.小波变换突破了传统傅里叶变换在时域和频域局部化方面的局限,非常适合对非平稳随机信号进行降噪滤波和特征提取.
The running status of the motor can be fully reflected by vibration signals, so the status features are usually extracted by analyzing vibration signals in the relative research. The vibration signals are non-stable and random, and the singularity parts of the signal often contain quite important information, so the examination for signal singularity is very important in the signal processing, Wavelet transform overcomes the disadvantage that the signal canl be localized both in time and frequency domain by Fourier Transform and it is quite suitable for deoising and feature extraction for non-stable and random signals. The results indicate the validity and applicability for the method above.
出处
《天津理工大学学报》
2009年第5期1-3,共3页
Journal of Tianjin University of Technology
基金
国家高技术研究发展计划(863计划)项目(2007AA041401)
天津市自然科学基金重点项目(08JCZDJC18600)
天津市科技计划项目(07ZHRDCG04500)
天津市高等学校科技发展基金项目(2006ZD32)
