Fault diagnosis for gear wear of planetary gearbox
LI Haiping1,2, ZHAO Jianmin2, ZHANG Xin2, NI Xianglong3
1.Institute of Systems Engineering, Academy of Military Sciences, Beijing, 100141, China;
2.Equipment Command and Management Department, Army Engineering University Shijiazhuang, 050003, China;
3.Luoyang Electronic Equipment Test Center, Luoyang, 471003, China
Abstract:To solve problems of planetary gearbox fault diagnosis method having higher professional requirements, complex calculation process and longer model training time, a new fault diagnosis method for planetary gearbox based on PCA-EDT-DBN was proposed.PCA was used to analyze vibration signals acquired with several sensors, select the first p principal components of each column of signals according to requirements, and arrange these p principal components into a one-dimensional (1-D) sequence.Euclidean distances between the first p principal components of each 2 columns data were computed to obtain a distance matrix.This matrix was sequentially expanded into a 1-D sequence.Two 1-D sequences obtained according to the mode mentioned above were synthesized into a 1-D one taken as a sample to be input into DBN for model training.Then, new samples were input into the trained model to output a classification result intelligently, and realize planetary gearbox’s fault diagnosis.Additionally, in order to improve the accuracy of model diagnosis, the orthogonal test method was used to optimize parameters of DBN.The preset fault test data for planetary gearbox teeth wear were used to verify the effectiveness of the proposed method.The results showed that the proposed method has advantages of higher diagnosis accuracy, shorter training time and simpler calculation process.
李海平1,2,赵建民2,张鑫2,倪祥龙3. 行星齿轮箱齿轮磨损故障诊断[J]. 振动与冲击, 2019, 38(23): 84-89.
LI Haiping1,2, ZHAO Jianmin2, ZHANG Xin2, NI Xianglong3. Fault diagnosis for gear wear of planetary gearbox. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(23): 84-89.
[1] 雷亚国, 何正嘉, 林京等. 行星齿轮箱故障诊断技术的研究进展[J]. 机械工程学报, 2011, 47(19): 59-67.
Lei Yaguo, He Zhengjia, Lin Jing, et al. Research advances of fault diagnosis technique for planetary gearboxes [J]. Journal of mechanical engineering, 2011, 47(19): 59-67. (in Chinese)
[2] 李海平, 赵建民, 宋文渊. 基于EMD-EDT的行星齿轮箱特征提取及状态识别方法研究[J], 振动与冲击, 2016, 35(3): 48-54.
Li Haiping, Zhao Jianmin, Song Wenyuan. Method of planetary gearbox feature extraction and condition recognition based on EMD and EDT [J]. Journal of vibration and shock, 2016, 35(3): 48-54. (in Chinese)
[3] 桂勇, 韩勤锴, 李峥等. 风机行星齿轮系统齿轮裂纹故障诊断[J], 振动、测试与诊断, 2016, 36(1): 169-175.
Gui Yong, Han Qinkai, Li Zheng, et al. The fault diagnosis of cracks in the planetary gear system of wind turbine [J]. Journal of vibration, measurement & diagnosis, 2016, 36(1): 169-175. (in Chinese)
[4] Cristian M V. Theoretical frequency analysis of vibrations from planetary gearboxes [J]. Forsch Ingenieurwes, 2012, 76: 15-31.
[5] 杨文广, 蒋东翔. 行星齿轮典型断齿故障的动力学仿真[J], 振动、测试与诊断, 2017, 37(4): 756-762.
Yang Wenguang, Jiang Dongxiang. Study of the dynamics of the planetary gear with typical tooth break faults [J]. Journal of vibration, measurement & diagnosis, 2017, 37(4): 756-762. (in Chinese)
[6] Liang X L, Zuo M J, Hoseini M R. Vibration signal modeling of a planetary gear set for tooth crack detection [J]. Engineering Failure Analysis, 2015, 48: 185-200.
[7] 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. (in Chinese)
[8] Paul D S, Darryll J P. A review of vibration-based techniques for helicopter transmission diagnostics [J]. Journal of sound and vibration, 2005, 282: 475-508.
[9] Liu Z L, Qu J, Zuo M J, et al. Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel fisher discriminant analysis [J]. International Journal of Advanced Manufacturing Technology, 2013, 67: 1217-1230.
[10] Feng Z P, Liang M, Zhang Y, et al. Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation [J]. Renewable Energy, 2012, 47: 112-126.
[11] 李军亮, 滕克难, 夏菲. 基于深度学习的军用飞机部件状态参数预测[J]. 振动与冲击, 2018, 37(6): 61-67.
Li Junliang, Teng Ke’nan, Xia Fei. Military aircraft components state parameters prediction using the deep belief learning [J]. Journal of vibration and shock, 2018, 37(6): 61-67. (in Chinese)
[12] He J, Yang S X, Gan C B. Unsupervised fault diagnosis of a gear transmission chain using a deep belief network [J]. Sensors, 2017, 17: 1564.
[13] Lei, Y G, Zuo, M J. Gear crack level identification based on weighted K nearest neighbor classification algorithm. Mechanical Systems and Signal Processing. 2009; 23; 1535-1547.
[14] Lebold, M.; McClintic, K.; Campbell, R.; Byington, C.; Maynard, K. Review of vibration analysis methods for gearbox diagnostics and prognostics. Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology. 2000, 623-634.
[15] Gharavian M H, Almas Ganj F, Ohadi A R, et al. Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes. [J]. Neurocomputing, 2013, 121: 150-159.
[16] Li H P, Zhao J M, Zhang X H, et al. Gear fault diagnosis and damage level identification based on Hilbert transform and Euclidean distance technique [J]. Journal of Vibroengineering, 2014, 16(8): 4137-4151.