A reliability analysis method of cloud theory – Monte Carlo based on performance degradation data

• Autorzy: Yang H. , Xu G. , Fan X.

Warianty tytułu

PL

Oparta na teorii chmury i modelu Monte Carlo metoda analizy niezawodnościowej danych o obniżeniu charakterystyk

• Języki publikacji: EN

Abstrakty

EN

Owing to inadequate degradation data, the randomness and the fuzziness of degradation processes, it is difficult to calculate the reliability of product. By investigating performance reliability using degradation data of performance, the authors proposed a method of analyzing reliability of performance degradation data using Monte Carlo principle and cloud theory. First of all, the performance degradation cloud with the degradation amount and the entropy which denotes the possible discrete degree of the degradation data, is generated by using performance degradation data and a cloud theory forward cloud generator. Then, the minimum membership threshold of cloud droplets and the threshold of product failure were set. Meanwhile, the number of cloud droplets that comply with the minimum membership degree and the failure threshold were counted. Finally, the reliability method of performance degradation data was proposed by using the principle of Monte Carlo and the cloud theory. In this work, the cloud theory was introduced to verify the reliability of the performance degradation of the product. The randomness and the fuzziness in the degradation tests are resolved. In addition, due to the limits of degradation test data, the difficulties in calculation of the reliability is resolved using the principle of Monte Carlo, the minimum membership of cloud droplets and its minimum degree are therefore guaranteed. This work provides a new method of simulating the reliability of degradation. The feasibility of the method was validated by an example ensuring a high durability of conveyor belt joints is tantamount to guaranteeing their reliable operation and that the results of research conducted so far fail to provide unambiguous solutions to a number of problems that emerge in this case, it is advisable that advanced studies using computer techniques should be conducted within this area.

PL

Ze względu na niewystarczające dane o degradacji oraz losowość i rozmycie procesów degradacji, obliczanie niezawodności produktu jest zadaniem trudnym. Chcąc badać niezawodność przy użyciu danych dotyczących obniżenia charakterystyk, autorzy zaproponowali metodę analizy danych o obniżeniu charakterystyk wykorzystującą zasady metody Monte Carlo oraz teorii chmury. Po pierwsze, wykorzystując dane o obniżeniu charakterystyk oraz progresywny generator chmur, wygenerowano chmurę obniżenia charakterystyk zawierającą dane na temat stopnia degradacji oraz stopnia entropii, która określa możliwy dyskretny stopień degradacji danych. Następnie, ustalono minimalny próg przynależności punktów chmury oraz próg uszkodzenia produktu. Policzono liczbę punktów chmury które spełniały warunek minimalnego stopnia przynależności oraz progu uszkodzenia. Wreszcie, zaproponowano metodę analizy niezawodnościowej danych o obniżeniu charakterystyk wykorzystującą zasady modelu Monte Carlo oraz teorii chmury. W pracy przedstawiono teorię chmury, która pozwala na weryfikację niezawodności danych of obniżeniu charakterystyk produktu. Rozwiązano w ten sposób problem losowości i rozmycia występujące w badaniach degradacji. Ponadto, przy użyciu metody Monte Carlo, rozwiązano trudności w obliczaniu niezawodności związane z ograniczeniami danych z badań degradacji, co zagwarantowało minimalną przynależność punktów chmury oraz minimalny stopień uszkodzenia. W prezentowanej pracy przedstawiono nową metodę symulacji niezawodności danych o degradacji. Poprawność przedstawionej metody zweryfikowano na podstawie przykładu. Zapewnienie wysokiej trwałości złączy taśmy przenośnikowej jest równoznaczne z zapewnieniem ich niezawodnej pracy, a ponieważ wyniki prowadzonych dotąd badań nie dostarczają jednoznacznych rozwiązań wielu wyłaniających się w tym przypadku problemów, wskazane jest prowadzenie w tym zakresie zaawansowanych badań z użyciem technik komputerowych.

Słowa kluczowe

EN

performance degradation; Cloud theory; Monte Carlo method; randomness; fuzziness

PL

obniżenie charakterystyk; teoria chmury; metoda Monte Carlo; losowość; rozmycie

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2015
• Tom: Vol. 17, no. 3
• Strony: 435--442
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Yang H.

• College of Mechanical Engineering Taiyuan University of science and Technology Waliu Road, No 66 Wanbolin District, Taiyuan, Shanxi Province, China

autor

Xu G.

• College of Mechanical Engineering Taiyuan University of science and Technology Waliu Road, No 66 Wanbolin District, Taiyuan, Shanxi Province, China

autor

Fan X.

• College of Mechanical Engineering Taiyuan University of science and Technology Waliu Road, No 66 Wanbolin District, Taiyuan, Shanxi Province, China

Bibliografia

• 1. Clarisse Sanon. Low temperature degradation and reliability of one-piece ceramic oral implants with a porous surface. Dental materials 2013; 29: 389-397, http://dx.doi.org/10.1016/j.dental.2013.01.007.
• 2. Daiana Antonio Da Silva, Eduardo Coelho Marques Da Costa, Jorge Luiz De Franco. Reliability of directly-molded polymer surge arresters:Degradation by immersion test versus electrical performance. Electrical Power and Energy Systems 2013; 53: 488-498, http://dx.doi.org/10.1016/j.ijepes.2013.05.023.
• 3. He L, Yin C, Ping W, Yuan R, Huang HZ. Reliability and risk assessment of aircraft electric systems. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16 (4): 497-506.
• 4. Huang RQ, Xi LF. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods.Mechanical Systems and Signal Processing 2007; 21: 193-207, http://dx.doi.org/10.1016/j.ymssp.2005.11.008.
• 5. Li DY, Liu CY. Study on the universality of the normal cloud model. Engineering Science 2004; 6(8): 28-34.
• 6. Li DY, Meng HJ, Shi XM. Membership clouds and membership cloud generators. Computers Research and Development 1995; 32(6): 16-21.
• 7. Li XJ, Liu LJ, Liu LZ. Evaluation of road network comprehensive reliability based on cloud theory and radar graph model. Application Research of Computer 2013; 30(10): 3007-3010.
• 8. Lv SJ, Zhang YS, Lou YH. Research of trusted technology based on cloud model. Application Research of Computer 2013; 30(8): 2523-2526.
• 9. Ma JM, Zhan XY. Performance reliability analysis of a Piston Pump affected by random degradation. Journal of Mechanical Engineering 2010; 46(14): 189-193, http://dx.doi.org/10.3901/JME.2010.14.189.
• 10. Min HH, Jeng SL, Shen PS. Assessing device reliability based on scheduled discrete degradation measurements. Probabilistic Engineering Mechanics 2009; 24: 151-158, http://dx.doi.org/10.1016/j.probengmech.2008.04.003.
• 11. M Nuhi, T Abu Seer, A Mal Tamimi. Reliability analysis for degradation effects of pitting corrosion in carbon steel pipes. Procedia Engineering 2011; 10: 1930-1935, http://dx.doi.org/10.1016/j.proeng.2011.04.320.
• 12. Ocak H, Loparo K A, Discenzo F M. Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling:A method for bearing prognostics. Journal of Sound and Vibration 2007; 302:951-961, http://dx.doi.org/10.1016/j.jsv.2007.01.001.
• 13. Olga Ink, Enrico Zio, Ulrich Weidmann. Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering and System Safety 2014; 121:198-206, http://dx.doi.org/10.1016/j.ress.2013.08.004.
• 14. Pan YN, Chen J, Li XL. Fuzzy c-means based equipment degradation assessment. Journal of Shanghai Jiaotong University 2009; 43(11): 1794-1797.
• 15. Pan ZQ, Narayanaswamy Balakrishnan. Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes. Reliability Engineering and System Safety 2011; 96: 949-957, http://dx.doi.org/10.1016/j.ress.2011.03.014.
• 16. Qiang ZY, Feng J, Liu Q, Zhou JL. Reliability analysis based on performance degradation model of compound Poisson-Normal process. Systems Engineering and Electronics 2006; 28(11): 1775-1778.
• 17. Qin Y, Ju XG, Lu Q. A new reliability evaluation method based on cloud theory and stochastic process. Information and Control 2012; 41(4): 454-458.
• 18. Qin Y, Lu Q, Huang ST. A method of system performance evaluation based on the cloud theory and the information fusion theory. Computer Engineering and science 2012; 34(2): 181-185.
• 19. Ren SH, Xue F, Yv WW. Reliability residual-life prediction method for thermal aging based on performance degradation. Nuclear Power Engineering 2013; 34(5): 96-99.
• 20. Siljak H, Subasi A. Fourier spectrum related properties of vibration signals in accelerated motor aging applicable for age determination. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16 (4): 616-621.
• 21. Sun ZQ, Zhao JY. Gamma process of degradation failure reliability analysis. Journal of Naval Aeronautical Engineering Institute 2010; 25(5): 581-584.
• 22. Tang JY, He P, Liang HQ. Comprehensive reliability assessment of long life products with correlated multiple failure modes. Journal of Mechanical Engineering 2013; 49(12): 176-182, http://dx.doi.org/10.3901/JME.2013.12.176.
• 23. Tan QN. The reliability modeling, analysis and comprehensive evaluation method of complex systems. PhD Thesis, Beijing: Beijing Jiaotong University, 2013.
• 24. Wang XD, Yi Z, Shen ZC. Proton radiation damage in ZnO-pigmented white paints and optical degradation mechanisms. Journal of Materials Engineering 2013; 3(5): 1-5.
• 25. Wang YJ, Jiang YC, Kang SQ. Diagnosis method of fault location and performance degradation degree of rolling bearing based on optimal ensemble EMD. Journal of Scientific Instrument 2013; 34(8): 1834-1840.
• 26. Wang Z, Pan R, Li XY, Jiang TM. A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information. Reliability Engineering and System Safety 2013; 112:38-47, http://dx.doi.org/10.1016/j.ress.2012.09.015.
• 27. Wu L, Zhang ZM, Meng XC. Application of cloud theory in reliability assessment of combat aircraft. Computer Simulation 2005; 22(7): 235-236.
• 28. Yang H, Xu GN. Reliability Analysis on the data of performance degradation based on the blind number theory. Journal of Mechanical Strength 2013; 35(6): 777-782.
• 29. Yang H, Xu GN. Reliability analysis on the data of Performance degradation based on the fuzzy threshold. Journal of Construction Machinery 2013; 11(4): 19-23.
• 30. Yang K, Xue J. Continuous states reliability analysis. Proc. Annual Reliability and Maintainability Symposium, Philadelphia, PA, USA, 13-16 January 1997: 175-176.
• 31. Yang WM, Sheng YX. Digital simulation of system reliability. Beijing: the Press of Beihang University, 1990.
• 32. Yang XM, Yuan JS, Wang JF. A new spatial forecasting method for distribution network based on cloud theory. Proceedings of the CSEE 2006; 26(6): 30-36.
• 33. Yang Z, Chen YX, Li YF, Kang R. Smart electricity meter reliability prediction based on accelerated degradation testing and modeling. Electrical Power and Energy Systems 2014; 56: 209-219, http://dx.doi.org/10.1016/j.ijepes.2013.11.023.
• 34. Yan JH, Lee J. Degradation assessment and fault modes classification using logistic regression. Journal of Manufacturing Science and Engineering Transactions of the ASME 2005; 127: 912-914, http://dx.doi.org/10.1115/1.1962019.
• 35. Zhao JY, Liu F. Reliability assessment from accelerated performance degradation tests. Journal of Harbin Institute of Technology 2008; 40(10): 1669-1671.
• 36. Zhao JY, Liu F, Sun Q. Reliability analysis of Metallized-film pulse capacitor under competing failure modes. Systems Engineering-Theory﹠Practice 2006; 26(1): 60-64.
• 37. Zhao JY. Study on reliability modeling and applications based on performance degradation. PhD Thesis, Changsha: China National University of Defense Technology, 2005.
• 38. Zhao JY, Sun Q, Zhou JL. Metallized film pulse capacitor based on the accelerated degradation data reliability analysis. Strong Laser and Particle Beams 2006; 18(9): 1495-1498.