基于参数化滤波的旋转设备特征频率提取

摘要
图/表
参考文献(17)
相关文章 (15)

全文: PDF (1407 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要本文针对强背景噪声下的特征提取问题，提出一种基于参数化滤波的旋转设备特征频率提取方法。首先，对目标特征频率进行瞬时频率的初步提取；其次，利用傅里叶基函数对初始瞬时频率进行拟合，得到所需特征频率的瞬时频率；最后，根据提取出的瞬时频率和瞬时幅值重构出提取后的频谱图，从而达到对噪声进行抑制并准确提取所需特征频率的目的。使用仿真信号验证了该方法的有效性，对齿轮传动系统中行星齿轮箱振动数据、轴承外圈故障振动数据及轴承外圈早期故障进行试验分析。结果表明，该方法能有效提高信号的信噪比，准确提取特征频率，增强故障特征。

	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
	位莎1
	杨阳2
	杜明刚2
	何清波1
	彭志科1
	3

关键词 ：参数化滤波, 特征频率提取, 故障诊断, 信号分解

Abstract：In this paper, a characteristic frequency extraction method of rotating machinery based on parametric filtering is proposed to solve the problem of feature extraction under strong background noise. Firstly, the instantaneous frequency of the target characteristic frequency is preliminary extracted. Secondly, the instantaneous frequency of the desired characteristic frequency is obtained by using the Fourier basis function to fit the initial instantaneous frequency. Finally, the Fourier spectrum is reconstructed according to the extracted instantaneous frequency and instantaneous amplitude, so as to achieve the purpose of suppressing noise and accurately extracting the required characteristic frequency. The effectiveness of the proposed method is verified by using the simulation signal. The vibration data of planetary gearbox in the gear transmission system, vibration data of bearing outer ring fault, and vibration data of bearing outer ring early fault are tested and analyzed. The results show that the proposed method can effectively improve the signal-to-noise ratio of the signal, accurately extract characteristic frequencies, and enhance fault features.

Key words： parametric filtering characteristic frequency extraction fault diagnosis signal decomposition

收稿日期: 2022-05-23 出版日期: 2023-09-15

引用本文:

位莎1，杨阳2，杜明刚2，何清波1，彭志科1,3. 基于参数化滤波的旋转设备特征频率提取[J]. 振动与冲击, 2023, 42(17): 203-209.
WEI Sha1, YANG Yang2, DU Minggang2, HE Qingbo1, PENG Zhike1,3. Feature frequency extraction of rotating equipment based on parameterized filtering. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(17): 203-209.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I17/203

[1] 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 Shengjie, PENG Zhenrui, YIN Hong. Fault Diagnosis of Gearbox Bearing Based on Multi-sensor Signal Processing[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2020(11): 5-10.
[2] 薛璇怡, 庞新宇. 基于1-DCNN的行星齿轮箱故障诊断[J]. 机械传动, 2020, 44(11): 127-133.
XUE Xuanyi, PANG Xinyu. Fault Diagnosis of Planetary Gearbox based on 1-DCNN[J]. Journal of Mechanical Transmission, 2020, 44(11): 127-133.
[3] CHEN Liquan, LIU Liansheng, HE Min, et al. Gearbox Fault Diagnosis Based on VMD and Acoustic Emission Technology[C]. 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand, 2019, pp. 1-6.
[4] 李延峰, 韩振南, 王志坚, 等. 基于VKF与阶比分析的滚动轴承故障特征提取方法[J]. 轴承, 2020(11): 56-62.
LI Yanfeng, HAN Zhennan, WANG Zhijian, et al. Extraction Method for Fault Feature of Rolling Bearings Based on VKF and Order Analysis[J]. Bearing, 2020(11): 56-62.
[5] 刘奇, 荆双喜, 冷军发, 等. 基于MODWPT和Choi-Williams分布的齿轮箱低频故障特征提取[J]. 机械设计与研究, 2020, 36(05): 96-100.
LIU Qi, JING Shuangxi, LENG Junfa, et al. Low Frequency Fault Feature Extraction of Gearbox Based on MODWPT and ChoiWilliams Distribution[J]. Machine Design & Research, 2020, 36(05): 96-100.
[6] LIANG Lin, LEI Zhe, LI Maolin, et al. Feature Extraction of Gearbox based on Order Analysis of Instantaneous Angular Speed[C]. 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), Enshi, 2018, pp. 498-501.
[7] 苏文胜, 王奉涛, 张志新, et al. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击, 2010, 29(3): 18-21.
SU Wensheng, WANG Fengtao, ZHANG Zhixin, et al. Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J]. Journal of Vibration and Shock, 2010, 29(3): 18-21.
[8] 李伟, 罗成. 基于VMD的行星齿轮箱故障特征提取新方法[J]. 噪声与振动控制, 2020, 40(03): 94-99.
LI Wei, LUO Cheng. A New Method for Fault Feature Extraction of Planetary Gearboxes Based on VMD[J]. Noise and Vibration Control, 2020, 40(03): 94-99.
[9] HUANG Jinying, LIU Lijun, PAN Hongxia, et al. Gearbox feature extraction and fault diagnosis based on blind source separation[C]. 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Chengdu, 2010, pp. V5-251-V5-255.
[10] 牛晓瑞, 张亢, 陈向民, 等. RS-LOD方法及其在旋转机械故障特征提取中的应用[J]. 振动与冲击, 2020, 39(16): 120-128.
NIU Xiaorui, ZHANG Kang, CHEN Xiangmin, et al. A RS-LOD method and its application in failure feature extraction of rotating machinery[J]. Journal of Vibration and Shock, 2020, 39(16): 120-128.
[11] FENG Yafang, YANG Yu. Research on the gearbox fault diagnosis based on SCS-BP neural network[C]. 2018 7th International Conference on Industrial Technology and Management (ICITM), Oxford, 2018, pp. 33-36.
[12] ZHANG Cheng, XU Liqing, LI Xingwang, et al. A Method of Fault Diagnosis for Rotary Equipment Based on Deep Learning[C]. 2018 Prognostics and System Health Management Conference (PHM-Chongqing), Chongqing, 2018, pp. 958-962.
[13] HAN Ji-Hoon, CHOI Dong-Jin, HONG Sun-Ki, et al. Motor Fault Diagnosis Using CNN Based Deep Learning Algorithm Considering Motor Rotating Speed[C]. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), Tokyo, Japan, 2019, pp. 440-445.
[14] TASTIMUR C, KARAKOSE M, AYDIN I, et al.. Defect Diagnosis of Rolling Element Bearing using Deep Learning[C]. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 2018, pp. 1-5.
[15] YEH C, CHEN R. Using Convolutional Neural Network for Vibration Fault Diagnosis Monitoring in Machinery[C]. 2018 IEEE International Conference on Advanced Manufacturing (ICAM), Yunlin, 2018, pp. 246-249.
[16] SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64-65: 100-131.
[17] QIU Hai, LEE Jay, LIN Jing. Wavelet Filter-based Weak Signature Detection Method and its Application on Roller Bearing Prognostics[J]. Journal of Sound and Vibration, 2006, 289(4-5): 1066-1090.