Volume 51 Issue 2
Feb.  2025
Turn off MathJax
Article Contents
ZHANG Z W,PENG C,CHE Z Y,et al. Servo drive unit reliability modeling with multi-stage degradation data fusion[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(2):692-704 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0200
Citation: ZHANG Z W,PENG C,CHE Z Y,et al. Servo drive unit reliability modeling with multi-stage degradation data fusion[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(2):692-704 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0200

Servo drive unit reliability modeling with multi-stage degradation data fusion

doi: 10.13700/j.bh.1001-5965.2023.0200
Funds:

National Natural Science Foundation of China (51875029); Supported by the Fundamental Research Funds for the Central Universities (YWF-23-PTJH-0701); Open Foundation of the Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology 

More Information
  • Corresponding author: E-mail:pch@cqjj8.com
  • Received Date: 22 Apr 2023
  • Accepted Date: 30 Aug 2023
  • Available Online: 12 Oct 2023
  • Publish Date: 11 Oct 2023
  • In order to accurately evaluate the reliability of the servo drive unit of a computer numerical control (CNC) system, a reliability modeling method with multi-stage degradation data fusion was proposed. Firstly, by analyzing multiple degradation process models, random effects were introduced, and a reliability model building scheme considering individual differences was given for the characteristics of individual differences existing between different servo drive units. Then, a Bayesian approach was adopted to fuse different degradation data by considering multiple degradation processes, and a reliability model of servo drive units with multi-stage degradation data fusion was established. Moreover, a Markov chain Monte Carlo (MCMC) method was used to complete the model parameter estimation. Finally, a servo drive unit loading test platform was built in the laboratory environment to collect experimental data and verify the validity of the model.

     

  • loading
  • [1]
    李丽, 张根保. “高档数控机床的高端制造模式” (二): 国产数控机床的竞争格局研究[J]. 计算机集成制造系统, 2023, 29(4): 1346-1356.

    LI L, ZHANG G B. The competitive pattern of domestic CNC machine tools[J]. Computer Integrated Manufacturing Systems, 2023, 29(4): 1346-1356 (in Chinese).
    [2]
    CASTANEDO F. A review of data fusion techniques[J]. The Scientific World Journal, 2013, 2013(1): 704504. doi: 10.1155/2013/704504
    [3]
    LO D, GOUBRAN R A, DANSEREAU R M. Robust joint audio-video talker localization in video conferencing using reliability information-II: Bayesian network fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2005, 54(4): 1541-1547. doi: 10.1109/TIM.2004.851071
    [4]
    司景萍, 马继昌, 牛家骅, 等. 基于模糊神经网络的智能故障诊断专家系统[J]. 振动与冲击, 2017, 36(4): 164-171.

    SI J P, MA J C, NIU J H, et al. An intelligent fault diagnosis expert system based on fuzzy neural network[J]. Journal of Vibration and Shock, 2017, 36(4): 164-171 (in Chinese).
    [5]
    MAJUMDER S, PRATIHAR D K. Multi-sensors data fusion through fuzzy clustering and predictive tools[J]. Expert Systems with Applications, 2018, 107: 165-172. doi: 10.1016/j.eswa.2018.04.026
    [6]
    CHEN Y, MA S, WEN X. Application of natural gradient algorithm for the aircraft engine vibration signal separation and fault diagnosis[J]. Journal of Convergence Information Technology, 2012, 7(12): 382-388. doi: 10.4156/jcit.vol7.issue12.43
    [7]
    孙伟超, 李文海, 李文峰. 融合粗糙集与D-S证据理论的航空装备故障诊断[J]. 北京亚洲成人在线一二三四五六区学报, 2015, 41(10): 1902-1909.

    SUN W C, LI W H, LI W F. Avionic devices fault diagnosis based on fusion method of rough set and D-S theory[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10): 1902-1909 (in Chinese).
    [8]
    INSUA D R, RUGGERI F, SOYER R, et al. Advances in Bayesian decision making in reliability[J]. European Journal of Operational Research, 2020, 282(1): 1-18. doi: 10.1016/j.ejor.2019.03.018
    [9]
    JEFFREYS H. Theory of probability[M]. 3d ed. Oxford: Clarendon Press, 1961.
    [10]
    LITTLEWOOD B, VERRALL J L. A Bayesian reliability model with a stochastically monotone failure rate[J]. IEEE Transactions on Reliability, 1974, R-23(2): 108-114. doi: 10.1109/TR.1974.5215217
    [11]
    MARTZ H F, WALLER R A. Bayesian reliability analysis of complex series/parallel systems of binomial subsystems and components[J]. Technometrics, 1990, 32(4): 407-416. doi: 10.1080/00401706.1990.10484727
    [12]
    PIEVATOLO A, RUGGERI F. Bayesian reliability analysis of complex repairable systems[J]. Applied Stochastic Models in Business and Industry, 2004, 20(3): 253-264. doi: 10.1002/asmb.522
    [13]
    ZAIDI A, OULD BOUAMAMA B, TAGINA M. Bayesian reliability models of Weibull systems: state of the art[J]. International Journal of Applied Mathematics and Computer Science, 2012, 22(3): 585-600. doi: 10.2478/v10006-012-0045-2
    [14]
    SIMON C, WEBER P, EVSUKOFF A. Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis[J]. Reliability Engineering & System Safety, 2008, 93(7): 950-963.
    [15]
    SOROUSH H, RASOULI V, TOKHMECHI B. A combined Bayesian–wavelet–data fusion approach for borehole enlargement identification in carbonates[J]. International Journal of Rock Mechanics and Mining Sciences, 2010, 47(6): 996-1005. doi: 10.1016/j.ijrmms.2010.06.015
    [16]
    徐兵, 姜艳青, 周志杰, 等. 基于贝叶斯估计的超声红外复合测距系统[J]. 解放军理工大学学报(自然科学版), 2013, 14(4): 365-371.

    XU B, JIANG Y Q, ZHOU Z J, et al. Ultrasonic infrared composite range system based on Bayesian estimation[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2013, 14(4): 365-371 (in Chinese).
    [17]
    彭卫文, 黄洪钟, 李彦锋, 等. 基于数据融合的加工中心功能铣头贝叶斯可靠性评估[J]. 机械工程学报, 2014, 50(6): 185-191. doi: 10.3901/JME.2014.06.185

    PENG W W, HUANG H Z, LI Y F, et al. Bayesian information fusion method for reliability assessment of milling head[J]. Journal of Mechanical Engineering, 2014, 50(6): 185-191 (in Chinese). doi: 10.3901/JME.2014.06.185
    [18]
    陈红霞, 张俊峰, 马爱博, 等. 基于改进贝叶斯的重型数控机床可靠性研究[J]. 电子科技大学学报, 2023, 52(1): 140-145. doi: 10.12178/1001-0548.2022153

    CHEN H X, ZHANG J F, MA A B, et al. Reliability research of heavy CNC machine tools based on improved Bayesian[J]. Journal of University of Electronic Science and Technology of China, 2023, 52(1): 140-145 (in Chinese). doi: 10.12178/1001-0548.2022153
    [19]
    GUO J Y, LI Y F, PENG W W, et al. Bayesian information fusion method for reliability analysis with failure-time data and degradation data[J]. Quality and Reliability Engineering International, 2022, 38(4): 1944-1956. doi: 10.1002/qre.3065
    [20]
    ZHAO Q, JIA X, CHENG Z J, et al. Bayesian estimation of residual life for weibull-distributed components of on-orbit satellites based on multi-source information fusion[J]. Applied Sciences, 2019, 9(15): 3017. doi: 10.3390/app9153017
    [21]
    CHEN Z, XIA T B, LI Y P, et al. Random-effect models for degradation analysis based on nonlinear tweedie exponential-dispersion processes[J]. IEEE Transactions on Reliability, 2022, 71(1): 47-62. doi: 10.1109/TR.2021.3107050
    [22]
    杨家鑫, 唐圣金, 李良, 等. 基于隐含非线性维纳退化过程的剩余寿命预测[J]. 北京亚洲成人在线一二三四五六区学报, 2024, 50(1): 328-340.

    YANG J X, TANG S J, LI L, et al. Remaining useful life prediction based on implicit nonlinear Wiener degradation process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024, 50(1): 328-340(in Chinese).
    [23]
    WANG X. Wiener processes with random effects for degradation data[J]. Journal of Multivariate Analysis, 2010, 101(2): 340-351. doi: 10.1016/j.jmva.2008.12.007
    [24]
    PENG C Y. Inverse Gaussian processes with random effects and explanatory variables for degradation data[J]. Technometrics, 2015, 57(1): 100-111. doi: 10.1080/00401706.2013.879077
    [25]
    EDWARDS K D, KONOLD T R. Impact of informative priors on model fit indices in Bayesian confirmatory factor analysis[J]. Structural Equation Modeling: A Multidisciplinary Journal, 2023, 30(2): 272-283. doi: 10.1080/10705511.2022.2126359
    [26]
    BARIBAULT B, COLLINS A G E. Troubleshooting Bayesian cognitive models[J]. Psychological Methods, 2023: 10.1037/met0000554.
    [27]
    AKAIKE H. A new look at the statistical model identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6): 716-723. doi: 10.1109/TAC.1974.1100705
    [28]
    SCHWARZ G. Estimating the dimension of a model[J]. The Annals of Statistics, 1978, 6(2): 461-464.
    [29]
    PANDEY A, SINGH A, GARDONI P. A review of Information Field Theory for Bayesian inference of random fields[J]. Structural Safety, 2022, 99: 102225. doi: 10.1016/j.strusafe.2022.102225
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(12)  / Tables(4)

    Article Metrics

    Article views(342) PDF downloads(20) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return