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Lifetime prediction method for MEMS gyroscope based on accelerated degradation test and acceleration factor model

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Metoda prognozowania czasu pracy żyroskopu MEMS na podstawie testu przyspieszonej degradacji i modelu współczynnika przyspieszenia
Języki publikacji
EN
Abstrakty
EN
The reliability analysis of MEMS gyroscope under long-term operating condition has become an urgent requirement with the enlargement of its application scope and the requirement of good durability. In this study we propose a lifetime prediction method for MEMS gyroscope based on accelerated degradation tests (ADTs) and acceleration factor model. Firstly, the degradation characteristic (bias instability) is extracted based on Allan variance. The effect of temperature stress on the degradation rate of bias instability is analyzed, and it shows that the degradation rate of bias instability would increase with the increase of the temperature. Secondly, the ADTs of MEMS gyroscope are designed and conducted, the degradation model of MEMS gyroscope is established based on the output voltage of MEMS gyroscope and Allan variance. Finally, the acceleration factor model of MEMS gyroscope under temperature stress is derived, and the lifetime of the MEMS gyroscope is predicted based on two group tests data under high stress level. The results show that the lifetime calculated by the acceleration factor model and mean lifetime under high stress levels is close to the mean lifetime calculated by the linear equation at normal temperature stress.
PL
Analiza niezawodności żyroskopu MEMS w warunkach długotrwałej pracy stała się pilną koniecznością wraz z rozszerzeniem zakresu jego zastosowania i wprowadzeniem wymogu dobrej trwałości. W niniejszym artykule, zaproponowano metodę prognozowania czasu pracy żyroskopu MEMS w oparciu o testy przyspieszonej degradacji i model współczynnika przyspieszenia. W pierwszej kolejności, wyznaczono charakterystykę degradacji (niestabilność wskazań) na podstawie wariancji Allana. Analizowano wpływ naprężenia cieplnego na szybkość degradacji w zakresie niestabilności wskazań. Analiza wykazała, że szybkość degradacji wzrastała wraz ze wzrostem temperatury. Następnie, opracowano i przeprowadzono testy przyspieszonej degradacji żyroskopu MEMS, a model jego degradacji ustalono na podstawie napięcia wyjściowego żyroskopu i wariancji Allana. Na koniec, wyprowadzono model współczynnika przyspieszenia dla żyroskopu MEMS w warunkach naprężenia cieplnego, a żywotność żyroskopu prognozowano na podstawie danych z dwóch testów grupowych przeprowadzonych w warunkach wysokiego naprężenia. Wyniki pokazują, że czas pracy obliczony na podstawie modelu współczynnika przyspieszenia i średni czas pracy przy wysokich poziomach naprężeń są zbliżone do średniego czasu pracy obliczonego na podstawie równania liniowego przy normalnym naprężeniu cieplnym.
Rocznik
Strony
221--231
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
  • Laboratory of Science and Technology on Integrated Logistics Support College of Intelligence Science and Technology National University of Defense Technology Yanwachi str., Changsha, 410073, Hunan, China, yaoliu133@126.com
autor
  • Laboratory of Science and Technology on Integrated Logistics Support College of Intelligence Science and Technology National University of Defense Technology Yanwachi str., Changsha, 410073, Hunan, China, wangyashun@nudt.edu.cn
autor
  • Laboratory of Science and Technology on Integrated Logistics Support College of Intelligence Science and Technology National University of Defense Technology Yanwachi str., Changsha, 410073, Hunan, China, fanzhengwei15@nudt.edu.cn
  • College of Intelligence Science and Technology National University of Defense Technology Yanwachi str., Changsha, 410073, Hunan, China, houzhanqiang@nudt.edu.cn
  • Laboratory of Science and Technology on Integrated Logistics Support College of Intelligence Science and Technology National University of Defense Technology Yanwachi str., Changsha, 410073, Hunan, China, sfzhang@nudt.edu.cn
autor
  • Laboratory of Science and Technology on Integrated Logistics Support College of Intelligence Science and Technology National University of Defense Technology Yanwachi str., Changsha, 410073, Hunan, China, chenxun@nudt.edu.cnj237
Bibliografia
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  • 4. Chateauneuf, and Alaa. Accelerated life testing and degradation modeling. Reliability Engineering System Safety 2014; 131: 228, https://doi.org/10.1016/j.ress.2014.05.004.
  • 5. Chen Wenhua, Liu Juan, Gao Liang, et al. Accelerated degradation reliability modeling and test data statistical analysis of aerospace electrical connector. Chinese Journal of Mechanical Engineering 2011; 24(6): 957-962, https://doi.10.3901/ CJME2011.06.957.
  • 6. Chien-Yu Peng and Sheng-Tsaing Tseng. Progressive-stress accelerated degradation test for highly-reliable products. IEEE Transactions on Reliability 2010; 59(1):30-37, https://doi. 10.1109/TR.2010.2040769.
  • 7. Chikovani V V, Kyiv, Ukraine. Performance parameters comparison of ring laser, coriolis vibratory and fiber-optic gyros based on Allan variance analysis. IEEE 2nd International Conference Actual Problems of Unmanned Air Vehicles Developments. Proceedings 2013; 153-156, https://doi.org/10.1109/APUAVD.2013.6705312.
  • 8. Chun-Lin Lu, Meng-Kao Yeh. Thermal stress analysis for a CMOS-MEMS microphone with various metallization and materials. Microelectronic Engineering 2019; 213: 47–54, https://doi.org/10.1016/j.mee.2019.04.013.
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  • 10. Fu-Kwun Wang, Tao-Peng Chu. Lifetime predictions of LED-based light bars by accelerated degradation test. Microelectronics Reliability 2012; 52(7):1332–1336, https://doi. 10.1016/j.microrel.2012.02.019.
  • 11. Grantham B E, Bailey M A. A least-squares normalized error regression algorithm with application to the Allan variance noise analysis method, In: Proceedings of IEEE/ION PLANS 2006; 750-755, https://doi.org/10.1109/PLANS.2006.1650671.
  • 12. Heera Mallik M, Divya Jyothi K, Mithun S Varma, Divya Rao A. Agrawal. Minimum Variance Optimal Filter Design for a 3x3 MEMS Gyroscope Cluster Configuration. IFAC 2016; 49.1: 639-645, https://doi.org/10.1016/j.ifacol.2016.03.128.
  • 13. Jacopo Iannacci. 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.
  • 14. Jianbin Su, Dingbang Xiao, Xiong Wang, Zhihua Chen, Xuezhong Wu. Vibration sensitivity analysis of the ‘Butterfly-gyro’ structure. Microsystem Technologies 2014; 20: 1281-1290, https://doi.org/10.1007/s00542-013-1913-x.
  • 15. Jin-Won Joo and Sung-Hoon Choa. Deformation behavior of MEMS gyroscope sensor package subjected to temperature change. IEEE Transactions on Components and Packaging Technologies 2007; 30.2: 346-354, https://doi.org/10.1109/TCAP T.2007.897948.
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  • 17. Kunsong Lin, Yunxia Chen, Dan Xu. Reliability assessment model considering heterogeneous population in a multiple stresses accelerated test. Reliability Engineering System Safety 2017; 165: 134-143, https://doi.org/10.1016/j. ress.2017.03.013.
  • 18. Lawrence C N, Darryll J P. Characterization of ring laser gyro performance using the Allan variance method. Journal of Guidance, Control, and Dynamics 1997; 20 (1): 211-214, https://doi.org/10.2514/2.4026.
  • 19. Liang Xue, Cheng-Yu Jiang, Hong-Long Chang, Yong Yang, Wei Qin, Wei-Zheng Yuan. A novel Kalman filter for combining outputs of MEMS gyroscope array. Measurement 2012; 45: 745-754, https://doi.org/10.1016/j.measurement.2011.12.016.
  • 20. 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, http://dx.doi.org/10.17531 /ein.2017.2.19.
  • 21. Miroslav Matejček, Mikulaš Šostronek. New Experience with Allan Variance Noise analysis of accelerometers. October 2017; https://doi.org/10.23919/KIT.2017.8109457.
  • 22. Mulloni V, Lorenzelli L, Margesin B, Barbato M, Meneghesso G. 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.
  • 23. Nelson W, Accelerated testing: statistical models, test plans, and data analysis. John Wiley & Sons: New York, 1990; NY. 10.1002/9780470316795.
  • 24. Ningfang S, Yuan R, Jin J. Autonomous estimation of Allan variance coefficients of onboard fiber optic gyro. Journal of Instrumentation 2011; 6.09: P09005-P09005, https://doi.org/10.1088/1748-0221/6/09/P09005.
  • 25. Oliveira V R B, Colosimo E A. Comparison of methods to estimate the time-to-failure distribution in degradation tests. Quality Reliability Engineering International 2004; 20(4): 363-73, https://doi.org/10.1002/qre.567.
  • 26. Pin Lv, Jianye Liu, Jizhou Lai, Kai Huang. Allan variance method for gyro noise analysis using weighted least square algorithm. Optik 2015; 126: 2529-2534, https://doi.org/10.1016/j.ijleo.2015.06.044.
  • 27. ReliaSoft Co. Accelerated Life Testing Reference. Tucson, AZ: ReliaSoft Publishing, 2014, https://www.ReliaSoft.com.
  • 28. Richard J V, Ahmed S K. Statistical modeling of rate gyros. IEEE Transactions on Instrumentation and Measurement 2012; 61: 673-684, https://doi.org/10.1109/tim.2011.2171609.
  • 29. Saeedivahdat A, Abdolkarimzadeh F, Feyzi A, Rezazadeh G, Tarverdilo S. Effect of thermal stresses on stability and frequency response of a capacitive microphone. Microelectronics Journal 2010; 41: 865-873.
  • 30. Si X S, Wang W B, Hu C H, Zhou D H, Pecht M G. 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.
  • 31. Songhua Hao, Jun Yang, Christophe Berenguer. Nonlinear step-stress accelerated degradation modeling considering three sources of variability. Reliability Engineering System Safety 2018; 172: 207-215, https://doi.org/10.1016/j.ress.2017.12.012.
  • 32. Tabata O, Tsuchiya T. Reliability of MEMS: testing of materials and devices. Weinheim, Wiley, 2006, https://doi.org/10.1007/s10800-008-9482-x.
  • 33. Wang YS, Fang X, Zhang CH, Chen X, Lu JZ. 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.
  • 34. 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.
  • 35. Yang Z, Chen Y X, Li X F, Zio E, and Kang R. Smart electricity meter reliability prediction based on accelerated degradation testing and modeling. Electrical Power and Energy Systems 2014; 56: 209-219, https://doi.org/10.1016/j.ijepes.2013.11.023.
  • 36. Zhiqiang X and Gebre-Egziabher D. Modeling and bounding low inertial sensor errors. In proc. IEEE/ION Position, Location and Navigation Symposium 2008: 1122-1132, https://doi.org/10.1109/PLANS.2008.4569999.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-42ab6c4e-a675-4d35-9c77-0f118fd1417b
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