|
|
|
| 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 |
| Military Vehicle Department, Army Military Transportation University, Tianjin 300161；China |
|
|
|
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.
|
|
Received: 12 January 2018
Published: 15 January 2019
|
|
|
|
[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.
|
|
|
|
