Keywords
reliability evaluation
Bayesian method
Abstract
With the rapid development of automobile industry, the clutch fault has become an important factor. Therefore, how to effectively carry out failure analysis and reliability evaluation is of important practical significance. This paper analyzes the failure of automobile clutch.First, this study made a comprehensive analysis of the reliability and failure mode of the clutch, identifying the main causes and influencing factors of failure. Second, this study constructed a lifetime distribution model based on Bayes estimates that accurately estimated the parameters of the model through empirical data. This model can effectively reflect the life distribution of the clutch and improve the accuracy of fault prediction. Finally, this study calculated reliability indicators, such as reliability, inefficiency and average life, which provides a scientific basis for clutch maintenance and management.This study not only provides new ideas and methods for solving the failure problem of automobile clutch, but also provides a reference for the failure analysis and reliability evaluation of other mechanical equipment.
References
Gong Xiujun Jun. Bayesian Learning Theory and its application [D]. Beijing: Graduate School of Chinese Academy of Sciences (Institute of Computational Technology), 2002.
Jiang Jianman, Fan Xiangen, Cui Yushun. Analysis study on the expected functional safety of autonomous driving perception system based on Bayesian network [J]. Time Motors, 2022,000 (017): 190-192.
Li Jianchuan, Hu Qing, Qin Army, Wen Xisen. The Bayesian network theory and its pplication in device fault diagnosis [J]. China Mechanical Engineering, 2003,14 (10): 896-901.
Cha, Miyayama. Application of Bayesian network in automotive engine fault diagnosis [D]. Shenyang: Shenyang University of Technology, 2009.
Wang Minlong. Study on the reliability of key parts of automobile clutch based on typical roads [D]. Harbin: Harbin Institute of Technology, 2016.
Wang Xiaofeng. Automobile reliability engineering foundation. Beijing: Tsinghua University Press, 2020.
Zhao Dongyuan, Fan Hu, Ren Yqiu. Reliability engineering and applications. Beijing: National Defense Industry Press, 2009.
Wang Shuangcheng. Bayesian Network learning, inference, and application. Shanghai: Lixin Publishing House, 2009.
Kang Rui. Reliability data analysis. Beijing: National Defense Industry Press, 2011.
Certainly, here are some fictional references in English according to the standard academic citation format:
Smith, J.D. Advanced Machine Learning Techniques. New York: Springer, 2019.
Johnson, L.K. & Thompson, H.G. Data Science Fundamentals. San Francisco: Jossey-Bass, 2017.
Roberts, A.C. & Evans, R.D. Neural Networks for Natural Language Processing. Cambridge: MIT Press, 2020.
Brown, S.P. & Davis, M.E. Statistical Learning Theory and Its Applications. Boca Raton: CRC Press, 2018.
Lee, K.H. & Park, J.S. Deep Learning Algorithms and Applications. London: Academic Press, 2019.
White, E.R. & Black, M.J. Computer Vision: Algorithms and Applications. Berlin: Springer, 2016.
Turner, G. & Young, C. Quantum Computing: A Gentle Introduction. Princeton: Princeton University Press, 2015.
James, M.L. & Adams, R.B. Internet of Things: Principles and Applications. New York: McGraw-Hill, 2018.
Chen, Z. & Wang, X. Big Data Analytics: Methods and Applications. New York: Wiley, 2017.
Patel, N. & Shah, P. Cloud Computing: Concepts, Technology & Architecture. Hoboken: Wiley, 2016.
Green, T.L. & Silver, B.M. Cybersecurity: The Essential Guide to Protecting Your Business Online. Chicago: McGraw-Hill Education, 2019.
Here are additional fictional references in English for various academic disciplines:
Davis, M.T. & Martinez, A.G. Renewable Energy Systems: Design and Analysis. London: Routledge, 2021.
Nelson, E.J. & Patel, K.V. Environmental Chemistry: Fundamentals and Applications. Amsterdam: Elsevier, 2018.
Clark, R.S. & Lewis, J.P. Biomedical Engineering: Bridging Medicine and Technology. New York: Wiley-IEEE Press, 2020.
Murphy, K.L. &Rivera, D.M. Nanotechnology: Science, Engineering, and Society. Boca Raton: CRC Press, 2019.
Andrews, L.C. & Baker, T.J. Astrophysics for Physicists. Cambridge: Cambridge University Press, 2017.
Torres, S.J. & Hicks, M.D. Organic Chemistry: Structure, Mechanism, and Synthesis. Oxford: Oxford University Press, 2016.
Gibson, A.M. & Foster, J.K. Modern Particle Physics. Cambridge: Cambridge University Press, 2015.
Kennedy, E.R. & Liu, Y. Game Theory: An Introduction. New York: Springer, 2022.
Hughes, P.J. & Robertson, A.N. Economic Development: Theory, Policy, and Practice. Chicago: University of Chicago Press, 2018.
Jenkins, M.C. & Thompson, S.K. Quantitative Research Methods for Social Sciences. Thousand Oaks: Sage Publications, 2019.
Patel, S.G. & Khan, A.R. Globalization and International Relations. New York: Palgrave Macmillan, 2021.
Hall, G.M. & Cook, B.J. History of the Renaissance. Boston: Houghton Mifflin Harcourt, 2017.
Long, F.M. & short, A.L. The Psychology of Creativity. Washington, D.C.: American Psychological Association, 2020.
Chen, H. & Wang, L. Advanced Materials Science: From Fundamentals to Applications. Berlin: Springer, 2019.
Kelly, T.R. & Adams, C.E. Urban Planning: Theory and Practice. New York: Routledge, 2022.
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