基于JRD和CUSUM的滚动轴承性能退化状态识别与评估

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (2) : 1-5.

论文

夏均忠，吕麒鹏，陈成法，刘鲲鹏，郑建波

作者信息 +

Performance degradation status identification and assessment of rolling element bearing based on the JRD and CUSUM

XIA Junzhong,L Qipeng,CHEN Chengfa,LIU Kunpeng,ZHENG Jianbo

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文章历史 +

摘要

退化特征提取是滚动轴承性能退化状态识别和评估的关键，JRD克服了传统特征无法准确反应轴承当前技术状态的不足，但在全寿命阶段上稳定性、单调性差，应用累积和（CUSUM）对其进行改进，从而准确识别和评估轴承性能退化状态。应用小波包变换对原始信号进行降噪；计算不同技术状态下信号的Renyi熵，并对比与标准状态的相似程度得出JRD值，作为滚动轴承退化状态特征；应用CUSUM增强JRD值对于寿命微弱变化的敏感性及轴承全寿命的单调性。通过试验验证，滚动轴承性能退化状态的识别率能达到100%，同时能够分阶段、单调性地评估轴承性能退化状态。

Abstract

Degradation feature extraction is the key of the identification and assessment of bearings’ degradation status.The JRD overcomes the shortcoming of traditionally used features that cannot accurately reflect the current status of bearing,but its stability and monotonicity are poor in the whole life period.So,the CUSUM was introduced to accurately identify and evaluate the status of bearing performance degradation.The wavelet packet transform was used to denoise the original signal,the Renyi entropy of different status of bearings was calculated,and the modified JRD value,adopted as a new bearings’ degradation feature,was calculated by comparing the similarity to the standard status.The CUSUM can enhance the sensitivity to weak changes and the monotonicity in the whole life.According to the experimental data,the recognition rate of the performance degradation of rolling bearings can reach 100% and the degradation status evaluation can be implemented by stages and in monotonicity.

导出引用

夏均忠，吕麒鹏，陈成法，刘鲲鹏，郑建波. 基于JRD和CUSUM的滚动轴承性能退化状态识别与评估[J]. 振动与冲击, 2019, 38(2): 1-5

XIA Junzhong,L Qipeng,CHEN Chengfa,LIU Kunpeng,ZHENG Jianbo. Performance degradation status identification and assessment of rolling element bearing based on the JRD and CUSUM[J]. Journal of Vibration and Shock, 2019, 38(2): 1-5

参考文献

[1] 夏均忠, 于明奇, 黄财, 等. 基于VMD和Infogram的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36(22):111-117.
Xia Jun-zhong, Yu Ming-qi, Huang Cai, et al. Fault feature extraction of rolling element bearing based on VMD and Infogram[J]. Journal of Vibration and Shock, 2017, 36(22):111-117.
[2] Rai A, Upadhyay S H. Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering[J]. Mechanical Systems and Signal Processing, 2017, 93:16-29.
[3] Shakya P, Kulkarni M S, Darpe A K. A novel methodology for online detection of bearing health status for naturally progressing defect[J]. Journal of Sound and Vibration, 2014, 333(21):5614-5629.
[4] Ali J B, Fnaiech N, Saidi L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015, 89(3):16-27.
[5] Pavle Boškoski, Đani Juričić. Fault detection of mechanical drives under variable operating conditions based on wavelet packet Rényi entropy signatures[J]. Mechanical Systems and Signal Processing, 2012, 31:369-381.
[6] Pavle Boškoski, Matej Gašperin, Dejan Petelin, et al. Bearing fault prognostics using Rényi entropy based features and Gaussian process models[J]. Mechanical Systems and ssignal Processing, 2015, 52-53:327-337.
[7] Singh J, Darpe A K, Singh S P. Bearing damage assessment using Jensen-Rényi Divergence based on EEMD[J]. Mechanical Systems and Signal Processing, 2017, 87:307-339.
[8] Akhand Rai, Upadhyay S H. The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings[J]. Measurement, 2017, 111:1-27.
[9] Cerrada M, Sánchez R, Li C, et al. A review on data-driven fault severity assessment in rolling bearings[J]. Mechanical Systems and Signal Processing, 2018, 99:169-196.
[10] 闫晓玲, 董世运, 徐滨士. 基于最优小波包Shannon熵的再制造电机转子缺陷诊断技术[J]. 机械工程学报, 2016, 52(4):7-12.
Yan Xiao-ling, Dong Shi-yun, Xu Bin-shi. Flaw diagnosis technology for remanufactured motor rotor based on optimal wavelet packet Shannon entropy[J]. Journal of Mechanical Engineering, 2016, 52(4):7-12.
[11] 马济通, 邱天爽, 李蓉,等. 脉冲噪声下基于Renyi熵的分数低阶双模盲均衡算法[J]. 电子与信息学报, 2017, 39(10):1-8.
Ma Ji-tong, Qiu Tian-shuang, Li Rong, et al. Dual-mode Blind Equalization Algorithm Based on Renyi Entropy and factional Lower Order Statistics Under Impulsive Noise[J]. Journal of Electronics and Information Technology, 2017, 39(10):1-8.
[12] Markel D, Zaidi H, Naqa I E. Novel multimodality segmentation using level sets and Jensen-Rényi divergence[J]. Medical Physics, 2013, 40(40):121908.
[13] 田再克, 李洪儒, 谷宏强,等. 基于局部特征尺度分解和JRD距离的液压泵性能退化状态识别方法[J]. 振动与冲击, 2016, 35(20):54-59.
Tian Zai-ke, Li Hong-ru, Gu Hong-qiang, et al. Degradation status identification of a hydraulic pump based on local characteristic-scale decomposition and JRD[J]. Journal of Vibration and Shock, 2016, 35(20):54-59.
[14] Gustavo Vedana, Filipe G Cardoso, Alexandre S Marcon, et al. Cumulative sum analysis score and phacoemulsification competency learning curve[J]. International Journal of Ophthalmology, 2017, 10(07):1088-1093.
[15] Center for Intelligent Maintenance Systems[EB/OL]. http://www.imscenter.net/, 2006.