针对最小熵解卷积(Minimum Entropy Deconvolution, MED)在故障诊断中倾向于恢复少量伪主导冲击以及依赖经验选取滤波器长度的问题,提出了一种增强自适应盲解卷积方法。该方法设计一种非线性变换以增强滤波信号中的故障冲击特征,并将其融入滤波器系数的迭代求解中,从而解决MED因少量伪主导冲击造成峭度过大而无法有效恢复周期性故障冲击的问题。同时,所提方法提供一种可根据待分析信号自适应获得合适滤波参数的策略,进而克服传统依赖经验取值的不足。仿真信号与齿轮植入故障信号分析结果验证方法对于增强故障冲击及自适应选取滤波参数的有效性,实现周期性故障冲击的准确恢复。在列车齿轮故障诊断的工程实际案例中,所提方法准确诊断出齿轮传动系统中大齿轮的早期裂纹故障。与MED的对比研究,进一步表明所提方法在故障冲击增强与自适应恢复方面的优势。
Abstract
Aiming at addressing the problems that minimum entropy deconvolution (MED) tends to restore a few pseudo-dominant impulses and determines the filter length empirically in fault diagnosis, an enhanced and adaptive blind deconvolution method is proposed in this paper. A nonlinear transformation is designed to process the filtered signal so as to enhance the fault impulses, and then the filter coefficients are estimated by maximizing the kurtosis of the processed signal. In this context, it can avoid attaining unsuitable filter coefficients due to excessively large kurtosis caused by a few pseudo-dominant impulses. Meanwhile, this method provides a strategy for adaptively obtaining the filter parameters according to the signal to be analyzed, and overcomes the drawback that depends on experience. The analysis results of simulated signals and gear seeded-fault signal verified the effectiveness of the method. In engineering applications, the method successfully diagnosed the incipient gear crack damage in a train transmission system, and showed great superiority over the traditional MED in enhancing and adaptively restoring the periodic fault impulses.
关键词
齿轮故障诊断 /
自适应盲解卷积 /
非线性变换 /
故障特征增强
{{custom_keyword}} /
Key words
gear fault diagnosis /
adaptive bind deconvolution /
nonlinear transformation /
fault impulse enhancement
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 中共中央国务院. 交通强国建设纲要[EB/OL]. 2019[2019-09-19]. http://xxgk.mot.gov.cn/jigou/zcyjs/201909/t20190920_3273715.html.
The Communist Party of China Central Committee and the State Council. Outline for building a leading transportation nation[EB/OL]. 2019[2019-09-19]. http://xxgk.mot.gov.cn/ jigou/zcyjs/201909/t20190920_3273715.html.
[2] 齐咏生,樊佶,李永亭,等. 一种改进的解卷积算法及其在滚动轴承复合故障诊断中的应用[J]. 振动与冲击,2020,39(21):140-150.
QI Yongsheng, FAN Ji, LI Yongting, et al. An improved deconvolution algorithm and its application in compound fault diagnosis of rolling bearing[J]. Journal of Vibration and Shock, 2020,39(21):140-150.
[3] ZHAI Wanming, LIU Pengfei, LIN Jianhui, et al. Experimental investigation on vibration behavior of CRH train at speed of 350 km/h[J]. International Journal of Rail Transportation, 2015, 3(1): 1-16.
[4] SKARLATOS D, KARAKASIS K, TROCHIDIS A. Railway wheel fault diagnosis using a fuzzy-logic method[J]. Applied Acoustics, 2004, 65(10): 951-966.
[5] 祝小彦,王永杰. 基于MOMEDA与Teager能量算子的滚动轴承故障诊断[J]. 振动与冲击,2018,37(06):104-110+123.
ZHU Xiaoyan, WANG Yongjie. Fault diagnosis of rolling bearings based on the MOMEDA and Teager energy operator[J]. Journal of Vibration and Shock, 2018,37(06):104-110+123.
[6] 乔志城,刘永强,廖英英. 改进经验小波变换与最小熵解卷积在铁路轴承故障诊断中的应用[J]. 振动与冲击,2021,40(02):81-90+118.
QIAO Zhicheng, LIU Yongqiang, LIAO Yingying. Application of improved wavelet transform and minimum entropy deconvolution in railway bearing fault diagnosis[J]. Journal of Vibration and Shock, 2021, 40(02):81-90+118.
[7] 徐金梧,徐科. 小波变换在滚动轴承故障诊断中的应用[J]. 机械工程学报,1997,33(4):50-55.
XU Jinwu, XU Ke. Application of wavelet transform in failure diagnosis of rolling bearings[J]. Journal of Mechanical Engineering, 1997, 33(4): 50-55.
[8] HUANG N E,SHEN Z,LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London, 1998, 454(1): 903-995.
[9] MALLAT S, ZHANG Zhifeng. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[10] PENG Z K, CHU F L. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography[J]. Mechanical Systems and Signal Processing, 2004, 18(2): 199-221.
[11] 何刘,林建辉,丁建明,等. 调幅-调频信号的经验模态分解包络技术和模态混叠[J]. 机械工程学报,2017,53(2):1-10.
HE Liu, LIN Jianhui, DING Jianming, et al. Empirical mode decomposition envelope technique and mode mixing problem in amplitude modulation-frequency modulation signals[J]. Journal of Mechanical Engineering, 2017, 53(2): 1-10.
[12] WU Z H, HUANG N E. Ensemble empirical mode decomposition: A noise assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1: 1-41.
[13] WIGGINS R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16(1): 21-35.
[14] MCDONALD G L, QING Zhao, ZUO Mingjian. Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing, 2012, 33(1): 237-255.
[15] BUZZONI M, ANTONI J, D'ELIA G. Blind deconvolution based on cyclostationarity maximization and its application to fault identification[J]. Journal of Sound and Vibration, 2018, 432: 569-601.
[16] MCDONALD G L, QING Zhao. Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection[J]. Mechanical Systems and Signal Processing, 2017, 82: 461-477.
[17] 姚金宝. 复杂工况下旋转机械故障特征提取方法研究[D]. 重庆:重庆大学,2017.
YAO Jinbao. Research on the methods of rotating machinery fault feature extraction under complex conditions[D]. Chongqing: Chongqing University, 2017.
[18] ABBOUD D, ELBADAOUI M, SMITH W A, et al. Advanced bearing diagnostics: A comparative study of two powerful approaches[J]. Mechanical Systems and Signal Processing, 2019, 114: 604-627.
[19] CHENG Yao, ZHOU Ning, ZHANG Weihua, et al. Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis[J]. Journal of Sound and Vibration, 2018, 425: 53-69.
[20] MIAO Yonghao, ZHAO Ming, LIN Jing, et al. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2017, 92: 173-195.
[21] CHEN Bingyan, ZHANG Weihua, SONG Dongli, et al. Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement[J]. Measurement (02632241), 2020, 159.
[22] YUAN Jing, HE Zhengjia, ZI Yanyang. Gear fault detection using customized multiwavelet lifting schemes[J]. Mechanical Systems and Signal Processing, 2010, 24(5): 1509-1528.
[23] RANDALL R B, ANTONI J. Rolling element bearing diagnostics—A tutorial[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 485-520.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}