基于时序模型和自联想神经网络的齿轮故障程度评估

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Abstract The gear damage severity evaluation underlines the prognostics and condition-based maintenance of mechanical systems.Being motivated by the fact that paradigms based on the probability similarity,like the hidden Markov model (HMM) and Gaussian mixed model (GMM),tend to an early saturation,a new approach for gear damage evaluation was proposed by making use of an autoregressive model (AR) and auto-associative neural network (AANN).The AR model was made to fit with gear vibration signals and the model coefficients were extracted as feature vectors which were then,fed to AANN to obtain reconstructed AR coefficients.A baseline AANN was trained by using the feature vectors from normal condition.The reconstructed AR coefficients by the baseline AANN will deviate from the original AR coefficients,if the gear condition degrade from normal condition.So,the difference between the residuals of AR models using reconstructed and original AR coefficients was exploited to formulate a gear damage indicator.Two experimental data sets involving one discrete damage-degree and one run-to-failure test were utilized to verify the proposed method.The results show the novel indicator is able to track damage progress with a consistent trend and to detect incipient damage in time.

Key words： AR model Auto-associative neural network Gears Fault severity Prognostics

Received: 24 August 2017 Published: 15 January 2019

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	Articles by authors
	ZHANG Long1
	2
	3
	CHENG Junliang2
	YANG Shixi1
	LI Xinglin3

Cite this article:

ZHANG Long1,2,3, et al. Fault severity assessment for gears based on AR model and auto-associative neural network[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(2): 18-24.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I2/18

[1] Ben Ali J, Chebel-Morello B, Saidi L, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J]. Mechanical Systems and Signal Processing, 2014, 56-57: 150-172.
[2] 雷亚国, 陈吴, 李乃鹏, 等. 自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用[J]. 机械工程学报, 2016, 56(01): 87-93.
LEI Ya-guo, CHEN Wu, LI Nai-peng, et al. A relevance vector machine prediction method based on adaptive multi-kernel combination and its application to remaining useful life prediction of machinery[J]. Journal of Mechanical Engineering, 2016, 56(01): 87-93.
[3] 张小丽, 陈雪峰, 李兵, 等. 机械重大装备寿命预测综述[J]. 机械工程学报, 2011, 47(11): 100-116.
ZHANG Xiao-li, CHEN Xue-feng, LI Bing, et al. Review of life prediction for mechanical major equipments[J]. Journal of Mechanical Engineering, 2011, 47(11): 100-116.
[4] 王奉涛, 陈旭涛, 柳晨曦, 等. 基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测[J]. 振动.测试与诊断, 2017, 37(03): 476-483.
WANG Feng-tao, CHEN Xu-tao, LIU Chen-xi, et al. Rolling bearing reliability assessment and life prediction based on KPCA and WPHM[J]. Journal of Vibration, Measurement & Diagnosis, 2017, 37(03): 476-483.
[5] 郭艳平, 颜文俊, 包哲静. 风力发电机组在线故障预警与诊断一体化系统设计与应用[J]. 电力系统自动化, 2010, 34(16): 83-86.
GUO Yan-ping, YAN Wen-jun, BAO Zhe-jing. Design and application of online fault warning and diagnosis integrated system for wind turbines[J]. Automation of Electric Power Systems, 2010, 34(16): 83-86.
[6] Wang Y, Liang M. An adaptive SK technique and its application for fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1750-1764.
[7] 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(03): 210-214.
LUO Yi, ZHEN Li-jing. Diagnosis method of turbine gearbox gearcrack based on wavelet packet and cepstrum analysis[J]. Journal of Vibration and Shock, 2015, 34(03): 210-214.
[8] Tang B, Liu W, Song T. Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution[J]. Renewable Energy, 2010, 35(12): 2862-2866.
[9] 马伦, 康建设, 孟妍, 等. 基于Morlet小波变换的滚动轴承早期故障特征提取研究[J]. 仪器仪表学报, 2013, 34(04): 920-926.
MA Lun, KANG Jian-she, MENG Yan, et al. Research on feature extraction of rolling bearing incipient fault based on Morlet wavelet transform[J]. Chinese Journal of Scientific Instrument, 2013, 34(04): 920-926.
[10] 于德介, 程军圣, 杨宇. 基于EMD和AR模型的滚动轴承故障诊断方法[J]. 振动工程学报, 2004, 17(03): 84-87.
YU De-jie, CHENG Jun-sheng, YANSG Yu. A fault diagnosis approach for roller bearings based on EMD method and AR model[J]. Journal of Vibration Engineering, 2004, 17(03): 84-87.
[11] 从飞云, 陈进, 董广明. 基于谱峭度和AR模型的滚动轴承故障诊断[J]. 振动.测试与诊断, 2012, 32(04): 538-541.
CONG Fei-yun, CHEN Jin, DONG Guang-ming. Spectral Kurtosis and AR Model Based Method for Fault Diagnosis of Rolling Bearings[J]. Journal of Vibration, Measurement & Diagnosis, 2012, 32(04): 538-541.
[12] Cheng J, Yu D, Yang Y. A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM[J]. EURASIP Journal on Advances in Signal Processing, 2008, 2008(1): 1-7.
[13] Heyns T, Heyns P S, de Villiers J P. Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox[J]. Mechanical Systems and Signal Processing, 2012, 32(4): 200-215.
[14] 曹端超, 康建设, 赵劲松, 等. 改进EMD和HMM的齿轮故障诊断方法应用[J]. 噪声与振动控制, 2013, 33(03): 208-211.
CAO Duan-chao, KANG Jian-she, ZHAO Jin-song, et al. Application of Gear Fault Diagnosis Method Based on Improved EMD and HMM[J]. Noise and Vibration Control, 2013, 33(03): 208-211.
[15] 王建利. 滚动轴承性能退化评价与趋势预测研究[D]. 大连理工大学, 2013.
WANG Jian-li. Research on performance degradation assessment and trend prediction of rolling bearing[D]. Dalian University of Technology, 2013.
[16] Kramer M A. Nonlinear Principal Component Analysis Using Autoassociative Neural Networks[J]. Aiche Journal, 1991, 37(37): 233-243.
[17] Wang D, Miao Q, Kang R. Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition[J]. Journal of Sound and Vibration, 2009, 324(3-5): 1141-1157.
[18] Rafiee J, Tse P W. Use of autocorrelation of wavelet coefficients for fault diagnosis[J]. Mechanical Systems and Signal Processing, 2009, 23(5): 1554-1572.
[19] Parey A, El Badaoui M, Guillet F, et al. Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect[J]. Journal of Sound and Vibration, 2006, 294(3): 547-561.
[20] 刘韬. 基于隐马尔可夫模型与信息融合的设备故障诊断与性能退化评估研究[D]. 上海交通大学, 2013.
LIU Tao. Study of Hidden Markov Model and information fusion in equipment fault diagnosis and performance degradation assessment[D]. Shanghai Jiao Tong University, 2013.