Multi-fault diagnosis method for wind turbine gearbox based on MOMEDA and enhanced cepstrum

PDF(2054 KB)

Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (7) : 268-273.

HU Aijun, YAN Jiaxiang, BAI Zerui

Author information +

History +

Abstract

The structure of wind turbine gearbox is complex. When there are many faults in gear and bearing, fault diagnosis accuracy is low and some faults’ diagnosis is missed due to different fault intensities, mutual coupling between faults and noise interference. A multi-fault diagnosis method for wind turbine gearbox based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and enhanced cepstrum was proposed. Firstly, fault characteristic frequencies of different positions of gear and bearing were used to set reasonable deconvolution period, and the original signal was preprocessed by using MOMEDA. Then, enhanced cepstrum was used to further suppress noise interference and enhance fault features. Finally, prominent components in enhanced cepstrum were compared with fault characteristic frequencies of gearbox to determine the fault type. The analysis results of multi-fault vibration test data of actual wind turbine gearbox showed that the proposed method can effectively extract multi-fault feature information of gearbox.

Key words

gearbox / multi-fault diagnosis / feature extraction / multipoint optimal minimum entropy deconvolution adjusted (MOMEDA） / enhanced cepstrum

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

HU Aijun, YAN Jiaxiang, BAI Zerui. Multi-fault diagnosis method for wind turbine gearbox based on MOMEDA and enhanced cepstrum[J]. Journal of Vibration and Shock, 2021, 40(7): 268-273

References

［1］陈向民, 于德介, 李蓉. 基于形态分量分析与阶次跟踪的齿轮箱复合故障诊断方法［J］. 航空动力学报, 2014, 29(1): 225-232.
CHEN Xiangmin, YU Dejie, LI Rong. Compound fault diagnosis method for gearbox based on morphological component analysis and order tracking［J］. Journal of Aerospace Power, 2014, 29(1): 225-232.
［2］王志坚. 齿轮箱多故障诊断特征提取的若干方法研究［D］.太原:太原理工大学,2015.
［3］TENG W, WU X, GAO Q F, et al. Cepstrum analysis of vibration signals of wind turbine gearbox［C］//30th Chinese Control Conference. Yantai, IEEE, 2011.
［4］SAWALHI N, RANDALL R B, ENDO H. The enhancement of fault detection and diagnosis in roiling element bearings using minimum entropy deconvolution combined with spectral kurtosis［J］. Mechanical Systems and Signal Processing, 2007, 21(6): 2616-2633.
［5］吕中亮. 基于变分模态分解与优化多核支持向量机的旋转机械早期故障诊断方法研究［D］.重庆:重庆大学,2016.
［6］WIGGINS R A. Minimum entropy deconvolution［J］. Geoexploration, 1978, 16(1/2): 21-35.
［7］WANG Z J, HAN Z N, GU F S, et al. A novel procedure for diagnosing multiple faults in rotating machinery［J］. ISA Transactions，2015(55): 208-218.
［8］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(6): 104-110.
ZHU Xiaoyan, WANG Yongjie. Fault diagnosis of rolling bearing based on the MOMEDA and Teager energy operator［J］. Journal of Vibrationand Shock, 2018, 37(6): 104-110.
［9］MCDONALD G L, ZHAO Q, ZUO M J. Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection［J］. Mechanical Systems and Signal Processing, 2012, 33(1): 237-255. 
［10］唐贵基, 王晓龙. 自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用［J］. 中国电机工程学报, 2015, 35(6): 1436-1444.
TANG Guiji, WANG Xiaolong. Adaptive maximum correlated kurtosis deconvolution method and application on incipient fault diagnosis of bearing［J］. Proceedings of the CSEE, 2015, 35(6): 1436-1444.
［11］王志坚, 王俊元, 赵志芳, 等. 基于MKurt-MOMEDA的齿轮箱复合故障特征提取［J］. 振动、测试与诊断, 2017, 37(4): 830-834.
WANG Zhijian, WANG Junyuan, ZHAO Zhifang, et al. Composite fault feature extraction of gear box based on Mkurt-MOMEDA［J］. Journal of Vibration, Measurement & Diagnosis, 2017, 37(4): 830-834.
［12］MCDONALD G L, ZHAO Q. Multipoint optimal minimum entropy deconvolution and convolution fix：application to vibration fault detection［J］. Mechanical Systerns and Signal Processing, 2016, 82: 461-477.
［13］夏均忠, 于明奇, 白云川, 等. 基于改进信息图与MOMEDA的滚动轴承故障特征提取［J］. 振动与冲击, 2019, 38(4): 26-32.
XIA Junzhong, YU Mingqi, BAI Yunchuan, et al. Fault feature extraction of rolling element bearing based on improved infogram and MOMEDA［J］. Journalof Vibration and Shock,2019, 38(4): 26-32.
［14］XU Y B, CAI Z Y, CAI X Y, et al. An enhanced multipion optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox［J］. Journal of Mechanical Science and Technology, 2019, 33(6): 2573-2586.
［15］罗毅, 甄立敬. 基于小波包与倒频谱分析的风电机组齿轮箱齿轮裂纹诊断方法［J］. 振动与冲击, 2015, 34(3): 210-214.
LUO Yi, ZHEN Lijing. Diagnosis method of turbine gearbox gearcrack based on wavelet packet and cepstrum analysis［J］. Journal of Vibration and Shock, 2015, 34(3): 210-214.
［16］张博. 直驱式风电机组轴承振动监测与故障诊断［D］.太原:太原理工大学,2014.
［17］孙伟，李新民, 金小强, 等. 基于MCKD 和增强倒频谱的直升机自动倾斜器滚动轴承故障诊断方法［J］. 振动与冲击，2019, 38(2): 159-163.
SUN Wei, LI Xinmin, JIN Xiaoqiang, et al. Fault diagnosis method for helicopter swash-plate rolling bearings based on the MCKD and envelope cepstrum［J］. Journal of Vibration and Shock, 2019, 38(2): 159-163.
［18］SHENG S. Wind turbine gearbox condition monitoring round robin study-vibration analysis［J］. Office of Scientific & Technical Information Technical Reports, 2012, 68: 856-860.