Fault diagnosis of planetary gearboxes based on adaptive parameter optimized ensemble local mean decomposition

PDF(3129 KB)

Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (18) : 60-69.

WANG Chaoge1，LI Hongkun1，YANG Rui1，REN Xueping2

Author information +

History +

Abstract

The selection mechanism of the two critical parameters (the amplitude of the added white noise and the number of ensemble trials) for adding white noise in the ensemble local mean decomposition is not clear. After adding white noises generate some tough problems including the pollution of the residue noise in the signal reconstruction, and the large computational cost. To overcome these problems, an adaptive noise parameter optimized ensemble local mean decomposition (APOELMD) is proposed in this paper. The method first adds a pair of pairs of high frequency positive and negative white noise in the process of local mean decomposition. The amplitude of the added white noise and the number of ensemble trials are respectively fixed as 0.01 times standard deviation of the original signal and two. Then the upper limit frequency of white noise is constantly changed, and the best upper limit frequency of white noise is selected by the index of relative root-mean-square error. After determining the best upper limit frequency of white noise, the APOELMD method can achieve the most ideal decomposition effect. The simulation results showed that the proposed method greatly improves the performance of ensemble local mean decomposition and improves the diagnostic efficiency. The method was applied to fault diagnosis of planetary gearbox, which could extract fault feature information accurately, and achieved accurate discrimination of local damage faults of planetary gearbox.

Key words

ensemble local mean decomposition / optimal upper bound frequency of white noise / parameter optimization / planetary gearboxes / feature extraction

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WANG Chaoge1，LI Hongkun1，YANG Rui1，REN Xueping2. Fault diagnosis of planetary gearboxes based on adaptive parameter optimized ensemble local mean decomposition[J]. Journal of Vibration and Shock, 2020, 39(18): 60-69

References

[1] LEI Y G, LIN J, ZUO M J, et al. Condition monitoring and diagnosis of planetary gearboxes: A review[J].Measurement, 2014, 48: 292-305.
[2] PARRA J, VICUNA C M. Two methods for modeling vibrations of planetary gearboxes including faults: comparison and validation[J]. Mechanical Systems andSignal Processing, 2017, 92: 213-225.
[3] 张东, 冯志鹏. 迭代广义短时Fourier变换在行星齿轮箱故障诊断中的应用[J].工程科学学报,2017,39(4):604-610.
ZHANG Dong, FENG Zhi-peng. Application of iterative generalized short-time Fourier transform to fault diagnosis of planetary gearboxes [J].Chinese Journal of Engineering,2017, 39(4): 604-610.
[4] 雷亚国, 汤伟, 孔德同, 等 .基于传动机理分析的行星齿轮箱振动信号仿真及其故障诊断[J].机械工程学报, 2014, 50(17):61-68
LEI Yaguo, TANG Wei, KONG Detong, et al. Vibration signal simulation and fault diagnosis of planetary gearboxes based on transmission mechanism analysis[J]. Journal of Mechanical Engineering, 2014, 50(17): 61-68
[5] ZHAO C, FENG Z P. Application of multi-domain sparse features for fault identification of planetary gearbox[J].Measurement, 2017, 104: 169-179.
[6] 王况, 王科盛, 左明健. 一种基于行星齿轮箱复合运动的故障诊断指标[J].振动与冲击, 2016, 35(23):216-221.
WANG Kuang, WANG Ke-sheng, ZUO Ming-jian. A fault diagnosis index based on complex movement of planetary gearbox [J].Journal of Vibration and Shock, 2016, 35(23): 216-221.
[7] Feng Z P, LIANG M, CHU F L. Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples[J].Mechanical Systems and Signal Processing, 2013,38 (1) :165-205.
[8] Li H, Hu Y, Li F, et al. Succinct and fast empirical mode decomposition[J].Mechanical Systems and Signal Processing, 2017, 85:879-895.
[9] LIU H M, ZHANG J C,CHENG Y J , et al. Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis[J].Journal of Sound and Vibration, 2016, 385: 350-371.
[10] 郑近德, 程军圣. 改进的希尔伯特-黄变换及其在滚动轴承故障诊断中的应用[J].机械工程学报, 2015, 51(01):138-145.
ZHENG Jin-de, CHENG Jun-sheng. Improved Hilbert-Huang transform and its applications to rolling bearing fault diagnosis[J].Journal of Mechanical Engineering,2015, 51(01):138-145.
[11] Smith J S. The local mean decomposition and its application to EEG perception data[J].Journal of the Royal Society Interface, 2005, 2(5) : 443-454.
[12] Chen X H, Cheng G, Shan X L, et al. Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance[J]. Measurement, 2015, 73:55-67.
[13] Cheng G, Chen X H, Li H Y, et al. Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition[J].Measurement, 2016, 91:140-154.
[14] Guo W, Tse P W. A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals[J]. Journal of Sound and
Vibration, 2013, 332(2):423-441.
[15] Yang Y，Cheng J S，Zhang K. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J]. Measurement, 2012, 45: 561-570.
[16] 王朝阁, 庞震, 任学平, 等. ELMD和MCKD在滚动轴承早期故障诊断中的应用[J].机械科学与技术, 2017, 36(11):1764-1770.
WANG Chao-ge, PANG Zhen, REN Xue-ping, et al. Early Fault diagnosis of roller bearing based on ensemble local mean decomposition and maximum correlated kurtosis deconvolution [J].Mechanical Science and Technology for Aerospace Engineering,2017, 36(11):1764-1770.
[17] Luo X, Huang X M. Fault Diagnosis of Wind Turbine based on ELMD and FCM[J].Open Mechanical Engineering Journal, 2014, 8(1):721-725.
[18] 廖星智, 万舟, 熊新. 基于ELMD与LS-SVM的滚动轴承故障诊断方法[J].化工学报, 2013, 64(12): 4667-4673.
LIAO Zhi-xing, WAN Zhou, XIONG Xin. Fault diagnosis method of rolling bearing based on Ensemble local mean decomposition and least squares support vector machine[J].CIESC Journal, 2013, 64(12): 4667-4673.
[19] Sun J D, Peng Z T, Wen J T. Leakage aperture recognition based on ensemble local mean decomposition and sparse representation for classification of natural gas pipeline[J]. Measurement, 2017, 108:91-100.
[20] 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 Zhi-peng, ZHAO Lei-lei, CHU Fu-lei. Vibration spectral characteristics of localized gear fault of planetarygearboxes[J]. Proceedings of the CSEE, 2013,33(5): 119-127.