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Reliability assessment for micro inertial measurement unit based on accelerated degradation data and copula theory

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With its extensive use in industry, assessing the reliability of the micro inertial measurment unit (MIMU) has become a pressing need. Unfortunately, the MIMU is made up of several components, and the degradation processes of each are intertwined, making it difficult to assess the MIMU’s reliability and remaining useful life. In this research, we offer a reliability assessment approach for the MIMU, which has long-lifetime and multiple performance characteristics (PCs), based on accelerated degradation data and copula theory.Each PC model of MIMU is constructed utilizing drift Brownian motion to depict accelerated degradation process. The copula function is used to model the multivariate dependent accelerated degradation test data and to describe the dependency between multiple MIMU performance parameters. The particle swarm optimization algorithm is used to estimate the unknown parameters in the multi-dependent ADT model. Finally, the storage test and simulation example on MIMU’s accelerated degradation data verify the feasibility and effectiveness of the proposed method.
Rocznik
Strony
554--563
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, Yanwachi str., Changsha, 410073, Hunan, China
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, Changsha, 410073, China
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, Yanwachi str., Changsha, 410073, Hunan, China
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, Changsha, 410073, China
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, Yanwachi str., Changsha, 410073, Hunan, China
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, Changsha, 410073, China
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, Yanwachi str., Changsha, 410073, Hunan, China
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, Changsha, 410073, China
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, Yanwachi str., Changsha, 410073, Hunan, China
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, Changsha, 410073, China
Bibliografia
  • 1. Aho K, Derryberry D, Peterson T. Model selection for ecologists: the worldviews of AIC and BIC. Ecology 2014; 95(3): 631–636, https://doi.org/10.1890/13-1452.1.
  • 2. Busababodhin P, Amphanthong P. Copula modelling for multivariate statistical process control: a review. Communications for Statistical Applications and Methods 2016; 23(6): 497–515, https://doi.org/10.5351/CSAM.2016.23.6.497.
  • 3. Chen B, Liu H, Wang H et al. Thermal shock induced nanocrack as high efficiency surface conduction electron emitter. Applied Surface Science 2011; 257(21): 9125–9128, https://doi.org/10.1016/j.apsusc.2011.05.111.
  • 4. Dong X, Huang Q, Xu W et al. Research on temperature characteristic of parasitic capacitance in MEMS capacitive accelerometer. Sensors and Actuators A: Physical 2019; 285: 581–587, https://doi.org/10.1016/j.sna.2018.12.001.
  • 5. Duong Q-H, Buchaillot L, Collard D et al. Thermal and electrostatic reliability characterization in RF MEMS switches. Microelectronics Reliability 2005; 45(9–11): 1790–1793, https://doi.org/10.1016/j.microrel.2005.07.095.
  • 6. Gu Y-K, Fan C-J, Liang L-Q, Zhang J. Reliability calculation method based on the Copula function for mechanical systems with dependent failure. Annals of Operations Research 2022; 311(1): 99–116, https://doi.org/10.1007/s10479-019-03202-5.
  • 7. Hao H, Su C. Bivariate Nonlinear Diffusion Degradation Process Modeling via Copula and MCMC. Mathematical Problems in Engineering 2014; 2014: 1–11, https://doi.org/10.1155/2014/510929.
  • 8. Iannacci J. Reliability of MEMS: A perspective on failure mechanisms, improvement solutions and best practices at development level. Displays 2015; 37: 62–71, https://doi.org/10.1016/j.displa.2014.08.003.
  • 9. Jordanger L A, Tjøstheim D. Model selection of copulas: AIC versus a cross validation copula information criterion. Statistics & Probability Letters 2014; 92: 249–255, https://doi.org/10.1016/j.spl.2014.06.006.
  • 10. Kang J, Lu Y, Zhao B et al. Remaining useful life prediction of cylinder liner based on nonlinear degradation model. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 24(1): 62–69, https://doi.org/10.17531/ein.2022.1.8.
  • 11. Le X L, Kim K, Choa S-H. Analysis of Temperature Stability and Change of Resonant Frequency of a Capacitive MEMS Accelerometer. International Journal of Precision Engineering and Manufacturing 2022; 23(3): 347–359, https://doi.org/10.1007/s12541-021-00602-1.
  • 12. Li S, Chen Z, Liu Q et al. Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review. IEEE Access 2020; 8: 74648–74678, https://doi.org/10.1109/ACCESS.2020.2987332.
  • 13. Lin W, Chai Y, Liu Q. Remaining Useful Life Prediction of Electronic Products Based on Wiener Degradation Process. IFAC-PapersOnLine 2019; 52(24): 24–28, https://doi.org/10.1016/j.ifacol.2019.12.372.
  • 14. Liu D, Wang S, Zhang C. Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process. Applied Mathematics and Computation 2022; 417: 126757, https://doi.org/10.1016/j.amc.2021.126757.
  • 15. Liu Y, Wang Y, Fan Z et al. Lifetime prediction method for MEMS gyroscope based on accelerated degradation test and acceleration factor model. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 221–231, https://doi.org/10.17531/ein.2020.2.5.
  • 16. Liu Y, Wang Y, Fan Z et al. A new universal multi-stress acceleration model and multi-parameter estimation method based on particle swarm optimization. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2020; 234(6): 764–778, https://doi.org/10.1177/1748006X20918793.
  • 17. Lu S, Lu H, Kolarik W J. Multivariate performance reliability prediction in real-time. Reliability Engineering & System Safety 2001; 72(1):39–45, https://doi.org/10.1016/S0951-8320(00)00102-2.
  • 18. Lu X, Chen X, Wang Y, Tan Y. Consistency analysis of degradation mechanism in step-stress accelerated degradation testing. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017; 19(2): 302–309, https://doi.org/10.17531/ein.2017.2.19.
  • 19. Marozau I, Auchlin M, Pejchal V et al. Reliability assessment and failure mode analysis of MEMS accelerometers for space applications. Microelectronics Reliability 2018; 88–90: 846–854, https://doi.org/10.1016/j.microrel.2018.07.118.
  • 20. Medjaher K, Skima H, Zerhouni N. Condition assessment and fault prognostics of microelectromechanical systems. Microelectronics Reliability 2014; 54(1): 143–151, https://doi.org/10.1016/j.microrel.2013.09.013.
  • 21. Miao J, Li X, Ye J. Predicting research of mechanical gyroscope life based on wavelet support vector. 2015 First International Conference on Reliability Systems Engineering (ICRSE), Beijing, China, IEEE: 2015: 1–5, https://doi.org/10.1109/ICRSE.2015.7366508.
  • 22. Mulloni V, Barbato M, Meneghesso G. Long-term lifetime prediction for RF-MEMS switches. Journal of Micromechanics and Microengineering 2016; 26(7): 074004, https://doi.org/10.1088/0960-1317/26/7/074004.
  • 23. Mulloni V, Lorenzelli L, Margesin B et al. Temperature as an accelerating factor for lifetime estimation of RF-MEMS switches. Microelectronic Engineering 2016; 160: 63–67, https://doi.org/10.1016/j.mee.2016.03.023.
  • 24. Pan D, Wei Y, Fang H, Yang W. A reliability estimation approach via Wiener degradation model with measurement errors. Applied Mathematics and Computation 2018; 320: 131–141, https://doi.org/10.1016/j.amc.2017.09.020.
  • 25. Peng W, Li Y-F, Yang Y-J et al. Bivariate Analysis of Incomplete Degradation Observations Based on Inverse Gaussian Processes and Copulas. IEEE Transactions on Reliability 2016; 65(2): 624–639, https://doi.org/10.1109/TR.2015.2513038.
  • 26. Pourhassan M R, Raissi S, Hafezalkotob A. A simulation approach on reliability assessment of complex system subject to stochastic degradation and random shock. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 370–379, https://doi.org/10.17531/ein.2020.2.20.
  • 27. Si X-S, Wang W, Hu C-H et al. Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process. IEEE Transactions on Reliability 2012; 61(1): 50–67, https://doi.org/10.1109/TR.2011.2182221.
  • 28. Skima H, Medjaher K, Varnier C et al. A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation. Microelectronics Reliability 2016; 65: 79–88, https://doi.org/10.1016/j.microrel.2016.07.142.
  • 29 Sun F, Fu F, Liao H, Xu D. Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula. Reliability Engineering & System Safety 2020; 204: 107168, https://doi.org/10.1016/j.ress.2020.107168.
  • 30. Wang X, Jiang P, Guo B, Cheng Z. Real-time Reliability Evaluation with a General Wiener Process-based Degradation Model: real-time reliability evaluation. Quality and Reliability Engineering International 2014; 30(2): 205–220, https://doi.org/10.1002/qre.1489.
  • 31. Wang Y, Fang X, Zhang C et al. Lifetime prediction of self-lubricating spherical plain bearings based on physics-of-failure model and accelerated degradation test. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(4): 528–538, https://doi.org/10.17531/ein.2016.4.7.
  • 32. Wang Y, Zhang C, Chen X, Tan Y. Lifetime prediction method for electron multiplier based on accelerated degradation test. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16(3): 484–490.
  • 33. Xu D, Xing M, Wei Q et al. Failure behavior modeling and reliability estimation of product based on vine-copula and accelerated degradation data. Mechanical Systems and Signal Processing 2018; 113: 50–64, https://doi.org/10.1016/j.ymssp.2017.07.035.
  • 34. Zhang Y, Ma Y, Ouyang L, Liu L. A novel reliability model for multi-component systems subject to multiple dependent competing risks with degradation rate acceleration. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(4): 579–589, https://doi.org/10.17531/ein.2018.4.9.
  • 35. Zhang Z, Si X, Hu C, Lei Y. Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods. European Journal of Operational Research 2018; 271(3): 775–796, https://doi.org/10.1016/j.ejor.2018.02.033.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b8a2fad0-002b-4728-a0b7-059157cf6d5a
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