提出了一种基于样本熵和分数阶傅里叶变换的故障特征提取新方法。这种方法的核心思想是首先把原始数据空间中可分性较差的数据映射到合适的分数阶空间，然后计算并比较经过合适阶次分数阶傅里叶变换后数据的样本熵，从而实现故障的特征提取。实测数据的研究结果表明，用这种方法提取信号特征，能增强不同故障模式的可分性，可以容易地将正常滚动轴承、内圈故障、外圈故障和滚动体故障的信号区分。
Abstract
In this paper a new method of fault feature extraction based on sample entropy and fractional Fourier transform is presented. The core of this new method is to map the original data with poor separability into the appropriate fractional space firstly. Then the sample entropies of the transformed data after fractional Fourier transformation with appropriate order are computed and compared, so that fault feature extraction is fulfilled. The results show this new method could enhance the separability of different failure modes, and discriminate the normal, inner ring fault, outer ring fault and roller fault signals distinctly.
关键词
样本熵 /
分数阶傅里叶变换 /
特征提取
{{custom_keyword}} /
Key words
sample entropy /
fractional Fourier transform /
feature extraction
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1]胥永刚, 何正嘉. 分形维数和近似熵用于度量信号复杂性的比较研究[J]. 振动与冲击, 2003, 22(3): 25-27.
XU Yong-gang, HE Zheng-jia. Research on comparison between approximate entropy and fractal dimension for complexity measure of signals [J]. Journal of vibration and shock, 2003, 22(3): 25-27.
[2]Yan R, Gao R X. Approximate entropy as a diagnostic tool for machine health monitoring[J]. Mechanical Systems and Signal Processing, 2007, 21(2): 824-839.
[3]Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278(6): H2039-H2049.
[4]Pincus S M. Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences, 1991, 88(6): 2297-2301.
[5]赵志宏, 杨绍普. 一种基于样本熵的轴承故障诊断方法[J]. 振动与冲击, 2012, 31(6): 136-140,.
ZHAO Zhi-hong, YANG Shao-pu. Sample entropy-based roller bearing fault diagnosis method [J]. Journal of vibration and shock, 2012, 31(6): 136-140,
[6]郑近德, 程军圣, 胡思宇. 多尺度熵在转子故障诊断中的应用[J]. 振动. 测试与诊断, 2013 (2): 294-297.
ZHENG Jin-de, CHENG Jun-sheng, HU Si-yu. Rotor fault diagnosis based on multiscale entropy [J]. Journal of vibration, measurement and diagnosis, 2013 (2): 294-297.
[7]Namias V. The fractional order Fourier transform and its application to quantum mechanics[J]. IMA Journal of Applied Mathematics, 1980, 25(3): 241-265.
[8]罗洁思, 于德介, 陈向民. 线调频小波路径追踪算法和FrFT相结合的升降速齿轮故障诊断方法[J]. 机械工程学报, 2012, 48(7): 56-61.
LUO Jie-si, YU De-jie, CHEN Xiang-min. Gear fault diagnosis under the speed-up and speed-down process based on the chirplet path pursuit algorithm and frft [J]. Journal of Mechanical Engineering, 2012, 48(7): 56-61.
[9]申永军, 杨绍普, 张光明. 基于分数Fourier变换的自适应信号降噪方法[J]. 振动工程学报, 2009, 22(3): 292-297.
SHEN Yong-jun, YANG Shao-pu, ZHANG Guang-ming. Adaptive nosie reduction method based on frational fourier transform [J]. Journal of vibration engineering, 2009, 22(3): 292-297.
[10]罗慧, 王友仁, 崔江. 基于最优分数阶傅里叶变换的模拟电路故障特征提取新方法[J]. 仪器仪表学报, 2009 (5): 997-1001.
LUO Hui, WANG You-ren, CUI Jiang. New approach to extract analog circuit fault features based on optimal fractional fourier transform [J]. Journal of Scientific Instrument, 2009 (5): 997-1001.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1325 KB)
