Dynamic Model Driving Fault Diagnosis Method for RV Reducer

doi:10.3901/JME.2025.11.194

Abstract

Abstract: The conditional monitoring of rotary vector (RV) reducers using machine learning has great significance to guarantee the reliability and security of RV reducer. However, in engineering applications, fault samples missing is rationale for restricting the development of artificial intelligence diagnosis. Because the problem of the insufficient fault sample of the RV reducer that have to be resolved, an intelligent diagnosis methods using dynamic model is proposed to detect faults in these reducers. In the first step, dynamic model of the healthy RV reducer is constructed, and pearson correlation coefficient (PCC)-based model updating technique is employed to obtain dynamic model with a certain precision. In the second step, the mathematical model of gear fault is established and inserted into the dynamic model to calculate the fault samples, which served as training samples of artificial intelligence diagnostic models to classify testing samples (fault to be diagnosed). Finally, the simulated fault samples are used as training samples, and the unknown fault samples obtained from test are utilized as test sample, fault classification using convolutional neural network (CNN) are obtained, which show that dynamic model driving fault diagnosis method for RV reducer can be guided to solve fault samples missing problem.

Key words: RV reducer, dynamic model, numerical simulation, artificial intelligent, fault diagnosis

CLC Number:

TP183
TH17

ZHENG Junkang, WANG Hui, XIANG Jiawei. Dynamic Model Driving Fault Diagnosis Method for RV Reducer[J]. Journal of Mechanical Engineering, 2025, 61(11): 194-207.

References

[1] 王田苗，陶永. 我国工业机器人技术现状与产业化发展战略[J]. 机械工程学报，2014，50(9)：1-13. WANG Tianmiao, TAO Yong. Research status and Industrialization development strategy of Chinese industrial robot[J]. Journal of Mechanical Engineering，2014，50(9)：1-13.
[2] 何卫东，李力行，李欣. 机器人用高精度RV减速器中摆线轮的优化新齿形[J]. 机械工程学报，2000，36(3)：51-55. HE Weidong，LI Lixing，LI Xin. New optimized tooth-profile of cycloidal gear of high precision RV reducer used in robot[J]. Journal of Mechanical Engineering，2000，36(3)：51-55.
[3] LI Xuan，LI Chaoyang，WANG Yawen, et al. Analysis of a cycloid speed reducer considering tooth profile modification and clearance-fit output mechanism[J]. Journal of Mechanical Design，2017，139(3)：1-12.
[4] WANG Lu，XIANG Jiawei，LIU Yi. Time-frequency- based maximum correlated kurtosis deconvolution approach for detecting bearing faults under variable speed conditions[J]. Measurement Science and Technology，2019，30(12)：125005-125012.
[5] LEI Yaguo，JIA Feng，LIN Jing，et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J]. IEEE Transactions on Industrial Electronics，2016，63(5)：3137-3147.
[6] AMAR M，GONDAL I，WILSON C. Vibration spectrum imaging: a novel bearing fault classification approach[J]. IEEE Transactions on Industrial Electronics，2015，62(1)：494-502.
[7] HUANG Guangbin，CHEN Lei. Enhanced random search based incremental extreme learning machine[J]. Neurocomputing，2008，71(16)：3640-3648.
[8] LI Xiaogang，FANG Yiming，LIU Le. Kernel extreme learning machine for flatness pattern recognition in cold rolling mill based on particle swarm optimization[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering，2020，42(5)：1-9.
[9] HAN Te，LIU Chao，YANG Wenguang, et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults[J]. Knowledge-Based Systems，2019，165(2)：474-487.
[10] 刘占生，窦唯，王东华，等. 基于遗传算法的旋转机械故障诊断方法融合[J]. 机械工程学报，2007，43(10)：227-233. LIU Zhansheng，DOU Wei，WANG Donghua，et al. Rotating machinery fault diagnosis combination of method based on genetic algorithm[J]. Journal of Mechanical Engineering，2007，43(10)：227-233.
[11] LI Pengfei，JIANG Yongying，XIANG Jiawei. Experimental investigation for fault diagnosis based on a hybrid approach using wavelet packet and support vector classification[J]. The Scientific World Journal，2014，2014(2)：1661-1667.
[12] HU Qin，QIN Aisong，ZHANG Qinghua, et al. Fault diagnosis based on weighted extreme learning machine with wavelet packet decomposition and KPCA[J]. IEEE Sensors Journal，2018，18(20)：8475-8483.
[13] 彭鹏，柯梁亮，汪久根. 噪声干扰下的RV减速器故障诊断[J]. 机械工程学报，2020，56(1)：30-36. PENG Peng，KE Liangliang，WANG Jiugen. Fault diagnosis of RV reducer with noise interference[J]. Journal of Mechanical Engineering，2020，56(1)：30-36.
[14] PENG Peng，WANG Jiugen. NOSCNN: A robust method for fault diagnosis of RV reducer[J]. Measurement，2019，138：652-658.
[15] ZHOU Xiaojun，SHAO Yimin，LEI Yaguo，et al. Time-varying meshing stiffness calculation and vibration analysis for a 16DOF dynamic model with linear crack growth in a pinion[J]. Journal of Vibration & Acoustics，2012，134(1)：192-197.
[16] LUO Wei，QIAO Baijie，SHEN Zhixian，et al. Investigation on the influence of spalling defects on the dynamic performance of planetary gear sets with sliding friction[J]. Tribology International，2021，154：1-14.
[17] HAN Qinkai，LI Xinglin，CHU Fulei. Skidding behavior of cylindrical roller bearings under time-variable load conditions[J]. International Journal of Mechanical Sciences，2018，135：203-214.
[18] JALAN A K，MOHANTY A R. Model based fault diagnosis of a rotor-bearing system for misalignment and unbalance under steady-state condition[J]. Journal of Sound and Vibration，2009，327(3)：604-622.
[19] YE Jun. Cosine similarity measures for intuitionistic fuzzy sets and their applications[J]. Mathematical and Computer Modelling，2011，53(1)：91-97.
[20] GU Xiaowei，ANGELOV P P，KANGIN D，et al. A new type of distance metric and its use for clustering[J]. Evolving Systems，2017，8(2)：1-11.
[21] LI Zengxian，LU Mao. Some novel similarity and distance measures of pythagorean fuzzy sets and their applications[J]. Journal of Intelligent and Fuzzy Systems，2019，37(1)：1-19.
[22] LIU Xiaoyang，HUANG Haizhou，XIANG Jiawei. A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine[J]. Knowledge-Based Systems，2020，195(1)：105653.
[23] 陈川. RV减速器静力学与动力学研究[D]. 天津：天津大学，2017. CHEN Chuan. Research on statics and dynamics of RV reducer[D]. Tianjin：Tianjin University，2017.
[24] WANG Hui，YANG Ronggang，XIANG Jiawei. Numerical simulation of gears for fault detection using artificial intelligence models[J]. Measurement，2022，203：111898.
[25] 雷亚国，罗希，刘宗尧，等. 行星轮系动力学新模型及其故障响应特性研究[J]. 机械工程学报，2016，52(13)：111-122. LEI Yaguo，LUO Xi，LIU Zongyao，et al. A new dynamic model of planetary gear sets and research on fault response characteristics[J]. Journal of Mechanical Engineering，2016，52(13)：111-122.
[26] CHEN Wei，LEI Yulong，FU Yao，et al. A study of effects of tooth surface wear on time-varying mesh stiffness of external spur gear considering wear evolution process[J]. Mechanism and Machine Theory，2021，155：104055.
[27] 万志国，訾艳阳，曹宏瑞，等. 时变啮合刚度算法修正与齿根裂纹动力学建模[J]. 机械工程学报，2013，49(11)：153-160. WAN Zhiguo，ZI Yanyang，CAO Hongrui，et al. Time-varying meshing stiffness algorithm modification and root crack dynamics modeling[J]. Journal of Mechanical Engineering，2013，49(11)：153-160.
[28] CHAARI F，FAKHFAKH T，HADDAR M. Analytical modelling of spur gear tooth crack and influence on gearmesh stiffness[J]. European Journal of Mechanicsa-Solids，2009，28(3)：461-468.
[29] SHEN Zhixian，QIAO Baijie，YANG Laihao，et al. Evaluating the influence of tooth surface wear on TVMS of planetary gear set[J]. Mechanism and Machine Theory，2019，136，206-223.
[30] XIANG Jiawei，ZHONG Yongteng. a novel personalized diagnosis methodology using numerical simulation and an intelligent method to detect faults in a shaft[J]. Applied Sciences，2016，6(12)：2-19.