Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (7): 1869-1875.doi: 10.13229/j.cnki.jdxbgxb.20230400

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Laser sequence pulse diagnosis method of planetary reducer fault based on high-speed photography technology

Chang-jian WANG(),Jiu-ming LIU,Jin-zhou ZHANG,Bin LI   

  1. School of Mechanical Engineering,Yangtze University,Jingzhou 434023,China
  • Received:2023-03-10 Online:2024-07-01 Published:2024-08-05

Abstract:

A diagnosis method of planetary gearbox laser sequence pulse based on high-speed photography technology was developed. The serial pulse laser was used with a high-speed camera to collect the serial pulse signal of the vibration laser of the gearbox, and the vibration signal was collected by multi-sensors to calculate the entropy of signal arrangement. To determine whether there are abnormal characteristics in the laser sequence pulse signal, if abnormalities are detected, the sequence pulse signal is converted into a non?separable linear relationship. Then, the radial basis function(RBF) kernel is utilized to calculate the pulse training values. Based on the level of these training values, the extent of faults in the planetary reducer's gears and bearings is assessed. The test results show that the research method is feasible and effective, and the characteristic points of probability density function are distributed in [0.23,0.34] by detecting the sequence pulse signals of different fault signals, which can accurately distinguish four different fault types.

Key words: high speed photography technology, planetary reducer, fault diagnosis, laser sequence pulse, fault feature extraction

CLC Number: 

  • TJ07

Fig.1

Schematic diagram of fault feature extraction of planetary gearbox"

Fig.2

Fault generation and transmission process in planetary reducer"

Fig.3

Fault diagnosis and detection process"

Fig.4

Experimental site map"

Fig.5

Pulse signal parameter values corresponding todifferent structural elements"

Fig.6

Pulse signal spectrum in case of planetary gear failure"

Fig.7

Probability density function of pulse signal of planetary reducer fault sequence"

1 樊家伟,郭瑜,伍星,等.基于LSTM神经网络和故障特征增强的行星齿轮箱故障诊断[J].振动与冲击,2021,40(20):271-277.
Fan Jia-wei, Guo Yu, Wu Xing, et al. Fault diagnosis of planetary gearboxes based on LSTM neural network and fault feature enhancement[J]. Journal of Vibration and Shock, 2021,40 (20): 271-277.
2 欧曙东,赵明,周涛,等.基于编码器信号的低转速行星齿轮箱故障诊断技术[J].中国电机工程学报,2021,41(5):1885-1894.
Shu-dong Ou, Zhao Ming, Zhou Tao, et al. Fault diagnosis technology for low-speed planetary gearbox based on encoder signals[J]. Proceedings of the CSEE, 2021,41(5): 1885-1894.
3 李涵钊,胡志芳,望昊.基于小波包分解的功率谱方法在人字门启闭机减速器故障诊断中的应用[J].水运工程,2020(2):117-123.
Li Han-zhao, Hu Zhi-fang, Wang Hao, et al. Application of power spectrum method based on wavelet packet decomposition in the fault diagnosis of reducer in the hoist of miter gate[J]. Port & Waterway Engineering, 2020(2): 117-123.
4 柳杨,王凌,高雁凤,等.工业机器人谐波减速器的传动误差超限故障诊断[J].机床与液压,2021,49(17):185-190.
Liu Yang, Wang Ling, Gao Yan-feng, et al. Transmission error over limit fault diagnosis for harmonic reducer of industrial robot[J]. Machine Tool & Hydraulics, 2021,49 (17): 185-190.
5 刘羊,宗望远,马丽娜,等.采用高速摄影技术测定油葵籽粒三维碰撞恢复系数[J].农业工程学报,2020,36(4):44-53.
Liu Yang, Zong Wang-yuan, Ma Li-na, et al. Determination of three-dimensional collision restitution coefficient of oil sunflower grain by high-speed photography[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020,36 (4): 44-53.
6 李宁宁,陈海洋,冯曼,等.多维立体化高速摄影测量系统实时精准控制技术[J].应用光学,2021,42(6):1062-1066.
Li Ning-ning, Chen Hai-yang, Feng Man, et al. Real-time precise control technology of multidimensional and stereoscopic high-speed photogrammetry system[J]. Journal of Applied Optics, 2021,42(6): 1062-1066.
7 张猛,苗长云,孟德军. 轴承早期故障特征提取方法研究[J].工矿自动化,2020,46(4):85-90, 116.
Zhang Meng, Miao Chang-yun, Meng De-jun. Research on a bearing early fault features extraction method[J]. Industry and Mine Automation, 2020,46(4): 85-90, 116.
8 衷路生,刘东东.多级神经网络的轴承故障诊断研究[J].计算机工程与应用,2020,56(7):193-199.
Zhong Lu-sheng, Liu Dong-dong. Research on bearing fault diagnosis of multi-level neural network[J]. Computer Engineering and Applications, 2020,56(7): 193-199.
9 陈仁祥,黄鑫,胡小林,等.多源信息深度融合的行星齿轮箱故障诊断方法[J].振动工程学报,2020,33(5):1094-1102.
Chen Ren-xiang, Huang Xin, Hu Xiao-lin, et al. Planetary gearbox fault diagnosis technique based on multi-source information deep fusion[J]. Journal of Vibration Engineering, 2020,33 (5): 1094-1102.
10 溥江,赵鑫,张秀华.基于动力学模型的少齿差行星轮减速器断齿点蚀故障分析[J].组合机床与自动化加工技术,2020(11):28-32.
Pu Jiang, Zhao Xin, Zhang Xiu-hua. Analysis of planetary wheel reducer based on dynamic model[J]. Chinese Journal of Scientific and Technical Periodicals, 2020 (11): 28-32.
11 Gao C, Yu Z Q, Zhou Q. Application of GA-ACO optimized BP neural network in fault diagnosis of planetary gearbox[J]. Journal of Mechanical Transmission, 2021,45(3): 153-160.
12 Yao M M, Tang X, Lu A. Research on fault diagnosis of planetary gearbox based on improved convolutional neural network[J]. Manufacturing Technology & Machine Tool, 2021(7): 141-145.
13 Zhu J, Deng A D, Deng M Q, et al.Fault diagnosis of planetary gearbox based on minimum entropy deconvolution and adaptive variational mode decomposition[J]. Journal of Southeast University(Natural Science Edition), 2020,50(4): 698-704.
14 Wu Z, Zhang Q, Huang H M.Research on fault diagnosis of compound planetary gear based on dynamic model and multiscale permutation entropy[J] .Machinery Design & Manufacture, 2020(9): 182-186.
15 熊俊,薛卫萍,姚志文.高强度铝合金表面激光熔覆温度场仿真[J].计算机仿真,2021,38(4):164-167, 197.
Xiong Jun, Xue Wei-ping, Yao Zhi-wen. Simulation of laser cladding temperature field on high strength aluminum alloy surface[J]. Computer Simulation, 2021,38(4): 164-167, 197.
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