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Research on Degradation Process and Continuity Reliability of Rolling Bearing Vibration Performance |
CHENG Li1,XIA Xin-tao1,2,MA Wen-suo1,2 |
1. School of Mechatronics Engineering,Henan University of Science and Technology, Luoyang, Henan 471003, China
2. Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province,Henan University of Science and Technology,Luoyang, Henan 471003,China |
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Abstract To investigate the relationship between the degradation process and the continuity reliability of rolling bearing vibration performance, the degradation model of rolling bearing vibration performance is proposed based on maximum entropy method and similarity method; then the continuity reliability model of rolling bearing vibration performance is established based on the maximum entropy method and poisson process; Finally, the Gray relation analysis is performed on the performance degradation sequence and the continuity reliability sequence of the rolling bearing. The experimental results show that the proposed degradation model of rolling bearing vibration performance can effectively identify the degenerative state of rolling bearings, and there is a clear gray relationship between the evolution processes of the continuity reliability and the degradation process of rolling bearing vibration performance, the credibility level exceeds 80%.
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Received: 19 January 2020
Published: 18 October 2021
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[1]陈剑, 庄学凯, 吕伍佯, 等. 基于IVMD 和马田系统的滚动轴承故障检测方法 [J]. 计量学报, 2019, 40 (6): 1083-1087.
Chen J, Zhuang X K, Lü W Y, et al. Fault Diagnosis of Rolling Bearing Using Mahalanobis-Taguchi System Based on IVMD [J]. Acta Metrologica Sinica, 2019, 40 (6): 1083-1087.
[2]张金凤, 李继猛, 杨莹, 等. 基于改进耦合增强随机共振的滚动轴承故障诊断[J].计量学报, 2019, 40 (3): 385-391.
Zhang J F, Li J M, Yang Y, et al. Rolling Bearing Fault Diagnosis Based on Improved Coupling-enhanced Stoch-astic Resonance [J]. Acta Metrologica Sinica, 2019, 40 (3): 385-391.
[3]孟宗, 邢婷婷, 张圆圆, 等. 基于关联维数和线段聚类的滚动轴承故障诊断[J]. 计量学报, 2019, 40 (1): 102-107.
Meng Z, Xing T T, Zhang Y Y, et al. Fault Diagnosis of Rolling Bearing Based on Correlation Dimension and Seg-ment Clustering [J]. Acta Metrologica Sinica, 2019, 40 (1): 100-105.
[4]王奉涛, 王贝, 敦泊森, 等. 改进Logistic回归模型的滚动轴承可靠性评估方法[J]. 振动、测试与诊断, 2018, 38 (1): 123-129.
Wang F T, Wang B, Dun B S, et al. Rolling Bearing Reliability Evaluation Based on Improved Logistic Regres-sion Model [J]. Journal of Vibration Measurement and Diagnosis, 2018, 38 (1): 123-129.
[5]金燕, 刘少军, 张建阁. 基于遗传算法优化的人工神经网络下高速滚动轴承的疲劳可靠性 [J]. 航空动力学报, 2018, 33 (11): 2748-2755.
Jin Y, Liu S J, Zhang J G. Fatigue reliability of high speed bearing based on genetic algorithm optimized artifi-cial neural network [J]. Journal of Aerospace Power, 2018, 33 (11): 2748-2755.
[6]康守强, 叶立强, 王玉静, 等. 基于数学形态学和IFOA-SVR的滚动轴承可靠度预测方法 [J]. 机械工程学报, 2017, 53 (8): 201-208.
Kang S Q, Ye L Q, Wang Y J, et al. Reliability prediction method of a rolling bearing based on mathematical morphology and IFOA-SVR[J]. Journal of Mech-anical Engineer, 2017, 53 (8): 201-208.
[7]OConnor P D T. Commentary: reliability-past, pres-ent, and future [J]. IEEE Transactions on Reliability, 2000, 49 (4): 335-341.
[8]Ali B 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, 2015, 56-57: 150-172.
[9]Zuo F J, Zhu S P, Gao Huiying, et al. Stochastic fatigue life and reliability prediction based on residual strength[J]. Journal of Shanghai Jiaotong University (Science), 2015, 20 (3): 331-337.
[10]Timofeev Y M, Khalatov E M. Estimation and Predic-tion of the Parametric Reliability of Electro-Pneumatic Automation Products [J]. Chemical and Petroleum Eng-ineering, 2015, 51 (3-4): 243-249.
[11]夏新涛, 叶亮, 孙立明, 等.滚动轴承性能保持可靠性预测[J]. 轴承, 2016, (6), 28-34.
Xia X T, Ye L, Sun L M, et al. Performance Continuity Reliability Prediction of Rolling Bearings [J]. Bearing, 2016, 6, 28-34.
[12]孟宗, 赵东方, 李晶, 等. 基于局部均值分解多尺度模糊熵和灰色相似关联度的滚动轴承故障诊断 [J].计量学报, 2018, 39 (2): 231-236.
Meng Z, Zhao D F, Li J, et al. Rolling Bearing Fault Diagnosis Based on Local Mean Decomposition Multi-scale Fuzzy Entropy and Grey Similar Incidence [J]. Acta Metrologica Sinica, 2018, 39 (2): 231-236.
[13]王冰, 胡雄, 李洪儒, 等. 基于基本尺度熵与GG模糊聚类的轴承性能退化状态识别[J]. 振动与冲击, 2019, 38 (5): 190-197.
Wang B, Hu X, Li H R, et al. Rolling bearing performance degradation state recognition based on basic scale entropy and GG fuzzy clustering. Journal of Vibr-ation and Shock, 2019, 38 (5): 190-197.
[14]许迪, 葛江华, 王亚萍, 等. 流形学习和M-KH-SVR的滚动轴承衰退预测[J]. 振动工程学报, 2018, 31 (5): 170-179.
Xu D, Ge J H, Wang Y P, et al. Prediction for rolling bearing performance degradation based on manifold lear-ning and M-KH-SVR [J]. Journal of Vibration Eng-ineer, 2018, 31 (5): 170-179.
[15]姜万录, 雷亚飞, 韩可, 等. 基于VMD和SVDD结合的滚动轴承性能退化程度定量评估[J]. 振动与冲击, 2018, 37 (22): 43-50.
Jiang W L, Lei Y F, Han K, et al. Performance degradation quantitative assessment method for rolling bearings based on VMD and SVDD. Journal of Vibration and Shock, 2018, 37 (22): 43-50.
[16]张波, 张景肖. 应用随机过程 [M]. 北京: 清华大学出版社, 2004.
[17]Politis D N. Nonparametric maximum entropy [J]. IEEE Transactions on Information Theory, 1993, 39 (4): 1409-1413.
[18]Liu J, Djurdjanovic D, Ni J, et al. Similarity based method for manufacturing process performance prediction and diagnosis [J]. Computers in Industry, 2007, 58 (6): 558-566.
[19]Yu J. Adaptive hidden Markov model based online learning framework for bearing faulty detection and performance degradation monitoring [J]. Mechanical Systems & Signal Processing, 2017, 83: 149-162.
[20]Guo L, Li N, Jia F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings [J]. Neurocomputing, 2017, 240: 98-109.
[21]邓聚龙. 灰理论基础 [M]. 武汉: 华中科技大学出版社, 2002.
[22]Smith W A, Randall R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64-65: 100-131. |
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