基于分数阶滤波的全息阶比解调谱及齿轮早期故障诊断

摘要
图/表
参考文献(11)
相关文章 (15)

全文: PDF (1232 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为提高变速器齿轮早期故障诊断的准确性和可靠性，提出了一种基于分数阶(fractional Fourier transform ,FRFT)滤波的全息阶比解调谱方法。对变速器变转速过程的水平与垂直测点振动包络信号进行分数阶滤波，分离出调制频率分量，再对分离后信号进行全息阶比谱分析，得到调制分量的全息阶比解调谱，并对齿轮早期点蚀故障进行故障诊断。试验结果表明：基于分数阶滤波的全息阶比解调谱，兼具分数阶滤波分离频率变化分量的特点与全息阶比谱融合多维特征的优势，既能有效分离变转速过程的调制分量，隔离其他分量和噪声干扰，又能有效融合两个方向的关键特征信息，有效诊断出单测点单特征方法难以识别的齿轮早期故障。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	梅检民1
	2
	常春2
	沈虹2
	赵慧敏2
	王双朋2

关键词 ：分数阶滤波, 全息阶比解调谱, 早期故障, 微弱特征

Abstract：In order to improve the accuracy and reliability of early fault diagnosis of transmission gear, a holographic- order envelop demodulation spectrum based on Fractional Fourier Transform (FRFT) filter is proposed. Firstly, the FRFT filter method is adopted to filter the vibration envelope signals in horizontal and vertical directions of the transmission acceleration process, and the modulation component is separated. Then the separated component is analyzed by order envelop demodulation spectrum, and the holographic- order envelop demodulation spectrum of modulation order component is obtained，and the pitting failure of gear is diagnosed. The experimental results show that the holographic order spectrum based on FRFT filtering combined with the characteristics of FRFT filtering to separate frequency variation components and the advantages of holographic order ratio spectrum fusion multi-dimensional features，it can not only effectively separate the modulation component from acceleration process, isolate other components and noise interference, but also effectively integrate the feature information of the two directions. Compared with the single measurement point single feature method, it is more effective in diagnosing the early pitting fault of the gear.

Key words： fractional Fourier transform (FRFT) filter Holographic order envelop demodulation spectrum Early failure Weak characteristics

收稿日期: 2022-03-22 出版日期: 2023-05-28

引用本文:

梅检民1，2，常春2，沈虹2，赵慧敏2，王双朋2. 基于分数阶滤波的全息阶比解调谱及齿轮早期故障诊断[J]. 振动与冲击, 2023, 42(10): 273-277.
MEI Jianmin1,2, CHANG Chun2, SHEN Hong2, ZHAO Huimin2, WANG Shuangpeng2. Holographic-order envelop demodulation spectrum based on FRFT filter and early fault diagnosis of gear . JOURNAL OF VIBRATION AND SHOCK, 2023, 42(10): 273-277.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I10/273

[1]李清名. 改进的自适应局部迭代滤波方法及其在齿轮箱故障诊断中的应用[D].湖南大学,2020.
Li Qingming. Modified adaptive local iterative method and its application to gearbox fault diagnosi[D].Hunan university,2020.
[2]陈向民,黎琦,张亢,晋风华,等. 变转速齿轮箱复合故障的自适应时变滤波分析[J]. 振动与冲击,2019,38(14):138-145.
CHEN Xiangmin，LI Qi，ZHANG Kang，Adaptive time-varying filtering method for gearbox compound fault diagnosis under variable rotational speed[J]. Journal of Vibration and shock, 2019,38(14):138-145.
[3]陈向民,黎琦,张亢. 基于自适应时频滤波的变转速齿轮故障特征提取[J]. 振动与冲击,2018,37(10):135-140+148.
CHEN Xiangmin，LI Qi，ZHANG Kang.Adaptive time-frequency filtering based fault characteristics extraction method for gears under variable rotational speed[J]. Journal of Vibration and shock, 2018,37(10):135-140+148.
[4]梅检民,贾继德,曾锐利. FRFT循频滤波及齿轮微弱故障特征提取[J]. 振动与冲击,2016,35(23):130-135+179.
MEI Jianmin,JIA Jide,ZENG Ｒuili. FＲFT Filtering based on frequency tracking and extraction of gear's weak fault features[J]. Journal of Vibration and shock, 2016,35(23):130-135+179.
[5]王圣杰,彭珍瑞,殷红. 基于多传感器信号处理的齿轮箱轴承故障诊断[J]. 组合机床与自动化加工技术,2020,(11):5-10.
WANG Sheng-jie，PENG Zhen-rui，YIN Hong. Fault Diagnosis of Gearbox Bearing Based on Multi-sensor Signal Processing[J].
Modular Machine Tool ＆ Automatic Manufacturing Technique, 2020,(11):5-10.
[6]龙霞飞,杨苹,郭红霞. 基于KELM和多传感器信息融合的风电齿轮箱故障诊断[J]. 电力系统自动化,2019,43(17):132-139.
Long Xiafei,Yang Ping, Guo Hongxia.Fault diagnosis of wind turbine gearbox based on KELM and Multi-sensor information fusion[J]. Automation of electric power systems, 2019,43(17):132-139.
[7]马越,陈捷,洪荣晶. 多传感器数据融合的风电齿轮箱性能衰退评估[J]. 计算机集成制造系统,2019,25(02):318-325.
Ma Yue,Chen Jie,Hone Rongjing.Performance degradation assessment of wind turbine generator gearbox based on multi-sensor information fusion[J]. Computer integrated manufacturing systems, 2019,25(02):318-325.
[8]刘栋材,郭杰,申远. 基于MEMD与全息谱技术的加速度传感器信息融合[J]. 中国科学技术大学学报,2016,46(08):702-708.
Liu Dongjie, Guo Jie, Shen Yuan. Information fusion of accelerometerrs based on the MEMD and holo-spectrum technology[J]. Journal of university of science and technology of China, 2016,46(08):702-708.
[9]李纪永,李舜酩,陈晓红,等. 基于HWPT-ZFFT的二维全息谱计算方法[J]. 振动.测试与诊断,2016,36(01):68-72+198.
Li Jiyong, Li Shunming, Chen Xiaohong. Research of 2-D holospectrum calculation based on HWPT-ZFFT Method[J].Journal of vibration ,Measurement＆Diagnosis, 2016,36(01):68-72+198.
[10]耿富礼,李富才,孟光. 二维全息谱在旋转机械故障诊断中的应用[J]. 机械科学与技术,2014,33(10):1445-1449.
Geng Fuli，Li Fucai，Meng Guang. Ｒesearch on Fault Diagnosis Using 2-D Holospectrum for Ｒotating Machinery[J]. Mechanical Science and Technology for Aerospace Engineering, 2014,33(10):1445-1449.
[11]梅检民,陈祥龙,肖云魁,等. 基于稳健全息阶比谱提取微弱故障特征[J]. 振动与冲击,2012,31(12):49-52+102.
MEI Jianmin,CHEN Xianglong,XIAO Yunkui. Weak fault characteristic extraction based on steady holographic-order spectrum[J]. Journal of vibration and shock, 2012,31(12):49-52+102.