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Similarity-based failure threshold determination for system residual life prediction

Treść / Zawartość
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Warianty tytułu
PL
Określanie progu awarii na podstawie podobieństwa jako metoda pozwalająca na przewidywanie trwałości resztkowej systemu
Języki publikacji
EN
Abstrakty
EN
An accurate determination of the system failure threshold is an essential requirement in achieving an appropriate system residual life prediction and a reasonable planned maintenance strategy optimization afterward for degradation systems. This paper proposes a failure threshold determination method based on quantitative measurement of the similarity between the operating system and the historical systems. The similarity is formulated by a weighted average function and then calculated by a convex quadratic formulation to minimizing the variance between the operating system and the historical systems. With an accurate determination of the system failure threshold in real-time, a better prediction of the residual life for the operating system is achieved. Finally, a real case study for several power-shift steering transmission systems monitored using oil spectral analysis is adopted to illustrate and numerically compare the improved performance of the proposed method.
PL
W przypadku systemów podlegających degradacji, dokładne określenie progu awarii systemu stanowi niezbędny warunek dokonania trafnej prognozy jego trwałości resztkowej oraz późniejszej optymalizacji strategii konserwacji rutynowych. W artykule zaproponowano metodę wyznaczania progu awarii opartą na ilościowym pomiarze podobieństwa między systemem użytkowanym obecnie a systemami użytkowanymi uprzednio. Podobieństwo formułuje się na podstawie funkcji średniej ważonej, a następnie oblicza na podstawie wypukłej formy kwadratowej w celu zminimalizowania wariancji między obecnie użytkowanym systemem a uprzednimi systemami. Dzięki dokładnemu określeniu progu awarii systemu w czasie rzeczywistym uzyskuje się lepszą prognostykę trwałości resztkowej obecnie użytkowanego systemu. W końcowej części pracy, w celu zilustrowania i numerycznego porównania ulepszonej wydajności proponowanej metody, zaprezentowano studium przypadku obejmujące kilka układów przeniesienia napędu monitorowanych przy użyciu analizy spektralnej oleju.
Rocznik
Strony
520--529
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District Beijing100081, China
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District Beijing100081, China
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District Beijing100081, China
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District Beijing100081, China
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District Beijing100081, China
Bibliografia
  • 1. Alaswad S, Xiang Y. A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering & System Safety 2017; 157: 54-63, https://doi.org/10.1016/j.ress.2016.08.009.
  • 2. Bian L, Gebraeel N, Kharoufeh J P. Degradation modeling for real-time estimation of residual lifetimes in dynamic environments. IIE Transactions 2015; 47(5): 471-486, https://doi.org/10.1080/0740817X.2014.955153.
  • 3. Caballé N C, Castro I T, Pérez C J, Lanza-Gutiérrez J. M. A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering & System Safety 2015; 134: 98-109, https://doi.org/10.1016/j.ress.2014.09.024.
  • 4. Chehade A, Bonk S, Liu K. Sensory-based failure threshold estimation for remaining useful life prediction. IEEE Transactions on Reliability2017; 66(3): 939-949, https://doi.org/10.1109/TR.2017.2695119.
  • 5. Chinnam R B. On-line reliability estimation for individual components using statistical degradation signal models. Quality and Reliability Engineering International 2002; 18(1): 53-73, https://doi.org/10.1002/qre.453.
  • 6. Du Y, Wu T, Makis V. Parameter estimation and remaining useful life prediction of lubricating oil with HMM. Wear 2017; 376: 1227-1233, https://doi.org/10.1016/j.wear.2016.11.047.
  • 7. Elwany A H, Gebraeel N Z. Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions 2008; 40(7): 629-639, https://doi.org/10.1080/07408170701730818.
  • 8. Giraitis L, Kapetanios G, Yates T. Inference on multivariate heteroscedastic time varying random coefficient models. Journal of Time Series Analysis 2018; 39(2): 129-149. https://doi.org/10.1111/jtsa.12271.
  • 9. Keizer M C O, Flapper S D P, Teunter R H. Condition-based maintenance policies for systems with multiple dependent components: A review. European Journal of Operational Research 2017; 261(2): 405-420, https://doi.org/10.1016/j.ejor.2017.02.044.
  • 10. Kim M J, Jiang R, Makis V, Lee C G. Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure. European Journal of Operational Research 2011; 214(2): 331-339, https://doi.org/10.1016/j.ejor.2011.04.023.
  • 11. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI 1995; 14(2): 1137-1145.
  • 12. Kozłowski E, Mazurkiewicz D, Żabiński T, Prucnal S, Sęp J. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (4): 679-685, https://doi.org/10.17531/ein.2019.4.18.
  • 13. Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications. Mechanical systems and signal processing 2014; 42(1-2): 314-334, https://doi.org/10.1016/j.ymssp.2013.06.004.
  • 14. Lei Y, Li N, Guo L, Li N, Yan T, Lin J. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing 2018;104: 799-834, https://doi.org/10.1016/j.ymssp.2017.11.016.
  • 15. Li X, Makis V, Zuo H, Cai J. Optimal Bayesian control policy for gear shaft fault detection using hidden semi-Markov model. Computers & Industrial Engineering 2018; 119: 21-35, https://doi.org/10.1016/j.cie.2018.03.026.
  • 16. Liao L. Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Transactions on Industrial Electronics 2013; 61(5): 2464-2472, https://doi.org/10.1109/TIE.2013.2270212.
  • 17. Liu, K., Gebraeel, N. Z., Shi, J. A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering 2013; 10(3): 652-664. https://doi.org/10.1109/TASE.2013.2250282
  • 18. Liu K, Huang S. Integration of data fusion methodology and degradation modeling process to improve prognostics. IEEE Transactions on Automation Science and Engineering 2014;13(1): 344-354, https://doi.org/10.1109/TASE.2014.2349733.
  • 19. Liu, X., Li, J., Al-Khalifa, K. N., Hamouda, A. S., Coit, D. W., Elsayed, E. A. Condition-based maintenance for continuously monitored degrading systems with multiple failure modes. IIE transactions 2013; 45(4): 422-435. https://doi.org/10.1080/0740817X.2012.690930
  • 20. Liu Y, Ma B, Zheng C S, Xie S Y. Failure prediction of power-shift steering transmission based on oil spectral analysis with Wiener process. Spectroscopy and Spectral Analysis 2015; 35(9): 2620-2624.
  • 21. Okoh C, Roy R, Mehnen J, Redding L. Overview of remaining useful life prediction techniques in through-life engineering services. Procedia CIRP 2014; 16: 158-163, https://doi.org/10.1016/j.procir.2014.02.006.
  • 22. Tang S J, Yu C Q, Feng Y B, Xie J, Gao Q H, Si X S. Remaining useful life estimation based on Wiener degradation processes with random failure threshold. Journal of Central South University 2016; 23(9): 2230-2241, https://doi.org/10.1007/s11771-016-3281-z.
  • 23. Tian Z, Wong L, Safaei N. A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mechanical Systems and Signal Processing 2010; 24(5): 1542-1555, https://doi.org/10.1016/j.ymssp.2009.11.005.
  • 24. Vališ D, Žák L, Pokora O, Lánský P. Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance. Reliability Engineering & System Safety 2016; 145: 231-242, https://doi.org/10.1016/j.ress.2015.07.026.
  • 25. Wakiru J M, Pintelon L, Muchiri P N, Chemweno P K. A review on lubricant condition monitoring information analysis for maintenance decision support. Mechanical Systems and Signal Processing 2019; 118: 108-132, https://doi.org/10.1016/j.ymssp.2018.08.039.
  • 26. Wang D, Tsui K L. Brownian motion with adaptive drift for remaining useful life prediction: Revisited. Mechanical Systems and Signal Processing 2018; 99: 691-701, https://doi.org/10.1016/j.ymssp.2017.07.015.
  • 27. Wang J, Makis V, Zhao X. Optimal condition-based and age-based opportunistic maintenance policy for a two-unit series system. Computers & Industrial Engineering2019; 134: 1-10, https://doi.org/10.1016/j.cie.2019.05.020.
  • 28. Xiao N, Huang H Z, Li Y, He L, Jin T. Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Engineering Failure Analysis 2011; 18(4): 1162-1170, https://doi.org/10.1016/j.engfailanal.2011.02.004.
  • 29. Yan S, Ma B, Zheng C. Degradation index construction methodology for mechanical transmission based on fusion of multispectral oil data. Industrial Lubrication and Tribology 2019; 71(2): 278-283, https://doi.org/10.1108/ILT-04-2018-0154.
  • 30. Yan S, Ma B, Zheng C. Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(1): 137-144, https://doi.org/10.17531/ein.2019.1.15.
  • 31. Yan S, Ma B, Wang X, Zheng C. Maintenance policy for oil-lubricated systems with oil analysis data. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2020; 22(3): 455-464, https://doi.org/10.17531/ein.2020.3.8.
  • 32. Yan S F, Ma B, Zheng C S. Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra. Advances in Mechanical Engineering 2018; 10(6): 1687814018784201, https://doi.org/10.1177/1687814018784201.
  • 33. Yan S F, Ma B, Zheng C S, Chen M. Weighted evidential fusion method for fault diagnosis of mechanical transmission based on oil analysis data. International Journal of Automotive Technology 2019; 20(5): 989-996, https://doi.org/10.1007/s12239-019-0093-9.
  • 34. Yan S F, Ma B, Zheng C S, Zhu L A, Chen J W, Li H Z. Remaining useful life prediction of power-shift steering transmission based on uncertain oil spectral data. Spectroscopy and Spectral Analysis 2019; 39(2): 553-558.
  • 35. Ye Z S, Xie M. Stochastic modelling and analysis of degradation for highly reliable products. Applied Stochastic Models in Business and Industry 2015; 31(1) 16-32, https://doi.org/10.1002/asmb.2063.
  • 36. Zhai Q, Ye Z S. Degradation in common dynamic environments. Technometrics 2018; 60(4): 461-471, https://doi.org/10.1080/00401706.2017.1375994.
  • 37. Zhang C, Lim P, Qin A K, Tan K C. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE transactions on neural networks and learning systems 2016; 28(10): 2306-2318, https://doi.org/10.1109/TNNLS.2016.2582798.
  • 38. 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 Research2018; 271(3): 775-796, https://doi.org/10.1016/j.ejor.2018.02.033
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a72b48ed-addf-4479-8d0f-52a483d69885
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