针对噪声干扰及复杂调制影响下的行星齿轮箱齿根裂纹故障检测,提出一种基于改进边带滤波和重构谱相关密度的行星齿轮箱齿根裂纹故障检测方法。首先应用振动分离技术对行星齿轮箱原始振动信号进行加窗截取,合成消除了时变传递路径影响的齿轮振动信号;对边带滤波技术进行改进后,在频域对该信号进行滑移边带滤波,再通过逆傅里叶变换将各频带滤波信号转换到时域并分别计算谱相关密度;最后将各频带信号的谱相关密度累加,获得齿轮振动信号的重构谱相关密度并以此检测行星齿轮箱齿根裂纹故障。通过正常、行星轮故障和太阳轮故障三种状态下的行星齿轮箱齿根裂纹故障实验分析表明,该方法能有效检测出行星轮、太阳轮齿根裂纹故障。
Abstract
Aiming at the fault detection of gear tooth crack in a planetary gearbox under the influence of noise and complex modulation interference, a fault extraction method based on improved sideband filtering and reconstructed spectral correlation density was proposed.Firstly, the vibration separation technology was applied to the vibration of the planetary gearbox to synthesize the gear vibration which has eliminated the influence of time-varying transmission path; After improving the sideband filtering, slide sideband filtering was performed on the vibration in the frequency domain, the filtered signals of each frequency band were converted into the time domain by the inverse Fourier transform and the spectral correlation density was calculated.Finally, the spectral correlation density of signal in each frequency band was accumulated to obtain the reconstructed spectral correlation density of the gear vibration and thereby detect the root crack failure.The experimental results of the planetary gearbox with tooth root crack under normal conditions, faulty planetary gear and faulty sun gear show that the method can detect the characteristics of faulty planetary and sun gears effectively.
关键词
行星齿轮箱 /
谱相关密度 /
边带滤波 /
齿根裂纹 /
振动分离
{{custom_keyword}} /
Key words
planetary gearbox /
spectral correlation density /
sideband filtering /
tooth root crack /
vibration separation
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 冯志鹏, 赵镭镭, 褚福磊. 行星齿轮箱齿轮局部故障振动频谱特征[J]. 中国电机工程学报, 2013, 33(5): 119-127.
Feng Zhipeng, Zhao Leilei, Chu Fulei. Vibration spectral characteristics of localized gear fault of planetary gearboxes[J]. China CSEE, 2013, 33(5): 119-127.
[2] J.Antoni. Cyclic spectral analysis in practice[J]. Mechanical Systems and Signal Processing, 2007, 21: 597-630.
[3] Capdessus C, Sidahmed M, Lacoume J L. Cyclostationary processes: Application in gear faults early diagnosis[J]. Mechanical Systems and Signal Processing, 2000, 14(3): 371-385.
[4] Raad A, J.Antoni, Sidahmed M.Indicators of cyclostationarity: theory and application to gear fault monitoring[J]. Mechanical Systems and Signal Processing, 2008, 22: 574-587.
[5] R.B.Randall, J.Antoni, S.Chobsaard. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals[J]. Mechanical Systems and Signal Processing, 2001, 15(5): 945-962.
[6] 李蓉, 于德介, 陈向民等. 基于阶次分析与循环平稳解调的齿轮箱复合故障诊断方法[J].中国机械工程, 2013, 24(10): 1320-1326.
Li rong, Yu Dejie, Chen Xiangmin, et al. Gearbox composite fault diagnosis method based on order analysis and cyclostationary demodulation[J]. China Mechanical Engineering, 2013, 24(10): 1320-1326.
[7] 王宏超,陈进,霍柏琦等. 强抗噪时频分析方法及其在滚动轴承故障诊断中的应用[J]. 机械工程学报, 2015, 51(1): 105-95.
Wang Hongchao, Chen Jin, Huo Baiqi, et al. Noise-resistant time-frequency analysis method and its application in fault diagnosis of rolling bearing [J]. Journal of mechanical engineering, 2013, 33 (17): 91-95.
[8] P. D. McFadden. A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox[J]. Journal of Sound and vibration, 1991, 144(1): 163-172.
[9] 赵磊, 郭瑜, 伍星. 基于振动分离信号构建和同步平均的行星齿轮箱轮齿裂纹故障特征提取[J]. 振动与冲击, 2018, 37(5): 142-147.
Zhao Lei, Guo Yu, Wu Xing. Fault feature extraction of gear tooth crack of planetary gear-box based on constructing vibration separation signals and synchronous average[J]. Journal of vibration and shock, 2018, 37(5): 142-147.
[10] 赵磊, 郭瑜, 伍星. 基于包络加窗同步平均的行星齿轮箱特征提取[J]. 振动、测试与诊断, 2019, 39(2): 321-326.
Zhao Lei, Guo Yu, Wu Xing. Fault Feature Extraction of Planetary Gearboxes Based on Angle Domain Windowed Synchronous Average of the Envelope Signal [J]. Journal of Vibration , Measurement & Diagnosis, 2019, 39(2): 321-326.
[11] Feng Zhipeng, Zhou Yakai, M.J. Zuo, et al. Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: a review with examples, Measurement, 2017, 103:106–132.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}