Engine residual technical life estimation based on tribo data

• Autorzy: Vališ D. , Zak L. , Pokora O.

Warianty tytułu

PL

Ocena technicznej trwałości resztkowej silnika w oparciu o dane tribologiczne

• Języki publikacji: EN

Abstrakty

EN

The aim of the paper is to estimate a system technical life. When estimating a residual technical life statistically, a big amount of tribo-diagnostic data is used. This data serves as the initial source of information. It includes the information about particles contained in oil which testify to oil condition as well as system condition. We focus on the particles which we consider to be interesting and valuable. This kind of information has good technical and analytical potential which has not been explored well yet. By modelling the occurrence of particles in oil we expect to find out when a more appropriate moment for performing preventive maintenance might come. The way of modelling and further estimation is based on the specific characteristics of a regression analysis, fuzzy logic and diffusion processes – namely the Wiener process. Following the modelling results we could, in fact, set the principles of “CBM – Condition Based Maintenance”. However, the possibilities are much wider, since we can also plan in service operation and mission. All these steps result in inevitable cost saving which we would like to contribute to.

PL

Celem pracy jest ocena trwałości technicznej układu. W ocenie statystycznej technicznej trwałości resztkowej, wykorzystywane są duże ilości danych tribo-diagnostycznych. Dane te służą jako początkowe źródło informacji. Dostarczają informacji nt. cząsteczek zawartych w oleju, które świadczą o jego bieżącym stanie, jak również o stanie całego układu. Szczególny nacisk położono na cząsteczki, które uznano za godne uwagi i wartościowe. Tego rodzaju informacje mają duży potencjał techniczny i analityczny, który nie został jeszcze wystarczająco zbadany. Modelując występowanie cząsteczek w oleju, spodziewamy się określić najlepszy czas na przeprowadzenie konserwacji zapobiegawczej. Sposób modelowania i dalszej oceny oparto o konkretne charakterystyki analizy regresji, logiki rozmytej i procesów dyfuzyjnych-tj.proces Wienera. Śledząc wyniki modelowania możliwe będzie ustalenie reguł utrzymania urządzeń zależnie od ich bieżącego stanu technicznego (condition-based maintenance, CBM). Możliwości są jednak dużo większe, pozwalając także na planowanie eksploatacji rutynowej i zadań. Wszystkie powyższe kroki prowadzą do oszczędności.

Słowa kluczowe

EN

field data assessment; off-line diagnostics; first hitting time; residual life; maintenance optimization

PL

analiza danych terenowych; diagnostyka off-line; czas pierwszego przejścia; trwałość resztkowa; optymalizacja eksploatacji

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2014
• Tom: Vol. 16, no. 2
• Strony: 203--210
• Opis fizyczny: Bibliogr. 36 poz., rys.

Twórcy

autor

Vališ D.

• Department of Combat and Special Vehicles Faculty of Military Technologies University of Defence Kounicova 65,662 10 Brno, Czech Republic

autor

Zak L.

• Department of Applied Mathematics Faculty of Mechanical Engineering Brno University of Technology Technicka 2896/2, 616 69 Brno, Czech Republic

autor

Pokora O.

• Department of Mathematics and Statistics Faculty of Natural Sciences Masaryk University Kotlarska 267/2, 611 37 Brno, Czech Republic

Bibliografia

• 1. Bartlett LM, Hurdle EE, Kelly EM, Intergrated System Fault Diagnostics Utilising Diagraph and Fault Tree-based Approaches 2009; Reliability Engineering and System Safety, 9(94), 1371-1380.
• 2. Chinnam RB, On-line reliability estimation for individual components using statistical degradation signal models. Quality and Reliability Engineering International 2002; 18, 53-73.
• 3. Chinnam RB. On-line reliability estimation of individual components, using degradation signals. IEEE Transactions on Reliability 1999; 4(48), 403-412.
• 4. Cornak S. Selected Problems of Drivers Microclimate. Proc. International Conference Transport Means 2009; Kaunas: University of Kaunas, 124-127.
• 5. Cornak S, Skolil J. The Selected Aspects of Life Fluids Evaluation. Proc. International Conference on Military Technologies, ICMT 2009, Brno: UO, 39-45.
• 6. Edleston OSST, Bartlett LM. A Tabu search algorithm applied to the staffing roster problem of Leicestershire police force. Journal of the Operational Research Society 2012; 4(63), 489-496.
• 7. Gebraeel N, Pan J. Prognostic Degradation Models for Computing and Updating Residual Life Distributions in a Time-Varying Environment.IEEE Transaction on Reliability 2008; 4(57), 539-550.
• 8. Gebrael NZ, Lawley MA, Li R, Ryan JK, Residual-life distribution from component degradation signals: A Bayesian approach 2005; IIE Transactions, 37, 543-557.
• 9. Ghasemi A, Hodkiewicz MR. Estimating Mean Residual Life for a Case Study of Rail Wagon Bearings. IEEE Transaction on Reliability 2012; 3(61), 719-730.
• 10. Ghasemi A, Soumaya SY, Ouali MS. Evaluating the Reliability Function and the Mean Residual Life for Equipment With Unobservable States. IEEE Transaction on Reliability 2010; 2(59), 426-439.
• 11. Ghasemi A, Yacout S, Ouali MS. Parameter Estimation Methods for Condition-Based Maintenance With Indirect Observations. IEEE Transaction on Reliability 2010, 2(59), 426-439.
• 12. Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance 2006. Mechanical Systems and Signal Processing, 7(20), 1483-1510.
• 13. Kharoufeh JP, Cox SM. Stochastic models for degradation-based reliability. IIE Transactions 2005; 37, 533-542.
• 14. Kim YS, Kolarik WJ. Real-time condition reliability prediction from on-line tool performance data. International Journal of Production Research 1992; 8(30), 1831-1844.
• 15. Koucky M, Valis D. Suitable approach for non-traditional determination of system health and prognostics. Zeszyty naukowe 2011; 1(159), 123-134.
• 16. Kumar EV, Chaturvedi SK, True degradation estimation of industrial equipment with fuzzy sets: a case study. Proceedings of the Institution of Mechanical Engineers Part O – Journal of Risk and Reliability 2009; 2(223), 167-179.
• 17. Labeau PE, Smidts C, Swaminathan S. Dynamic reliability: Towards an integrated platform for probabilistic risk assessment. Reliability Engineering and System Safety 2010; 3 (68), 219-254.
• 18. Li W, Pham H, An inspection-maintenance model for systems with multiple competing processes. IEEE Transactions on Reliability 2005; 2(54), 318-327.
• 19. Lu H, Kolarik WJ, Lu SS. Real-time performance reliability prediction. IEEE Transactions on Reliability 2001; 4(50), 353-357.
• 20. Lu S, Lu H, Kolarik WJ. Multivariate performance reliability prediction in real-time. Reliability Engineering and System Safety 2001; 72,39-45.
• 21. Maillart LM, Pollock SM. Cost-optimal condition-monitoring for predictive maintenance of 2-phase systems. IEEE Transactions on Reliability 2002; 3(51), 322-330.
• 22. Mamdani EH, Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, 12(26), 1182-1191.
• 23. Medjaher K, Tobon-Mejia DA, Zerhouni N. Remaining Useful Life Estimation of Critical Components With Application to Rearing. IEEE Transaction on Reliability 2012; 2(61), 292-302.
• 24. Park KS. Condition-based predictive maintenance by multiple logistic function. IEEE Transactions on Reliability 1993; 4(42), 556-560.
• 25. Rak J, Pietrucha K. Risk in drinking water quality control. Przemysl Chemiczny 2008; 5(87), 554-556.
• 26. Revie M, Bedford T, Walls L. Supporting Reliability Decisions During Defence Procurement Using a Bayes Linear Methodology. IEEE Transactions on Engineering Management 2011; 4(58), 662-673.
• 27. Shafti F, Bedford T, Deleris LA, Hosnins JRM, Shen H, Walls L. Service operation classification for risk management. IBM Journal of Research and Development 2010; 3(54), 662-673.
• 28. Si XS, Wang W, HuCh H, Zhou DH, Pecht MG. Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process. IEEE Transaction on Reliability 2012; 1(61), 50-67.
• 29. Stodola J, Stodola P. Mechanical System Wear and Degradation Process Modelling. Transactions of Famena 2010; 4(34), 19-32.
• 30. Stodola P, Jamrichova Z, Stodola J. Modelling of Erosion Effects on Coating of Military Vehicles Components. Transactions of Famena 2012; 3(36), 33-44.
• 31. Sugeno M. Industrial applications of fuzzy control. Elsevier Science Pub. Co., 1985.
• 32. Titrou A, Bedford T, Walls L. Bayes geometric scaling model for common cause failure rates.Reliability Engineering and System Safety 2010; 2(95), 70-76.
• 33. Toms LA, Toms AM. Machinery Oil Analysis - a Guide for Maintenance Managers, Supervisors and Technicians. Society of Tribologists and Lubrication Engineers 2008.
• 34. Vališ D, Koucký M, Žák L. On approaches for non-direct determination of system deterioration. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2012; 1(14), 33-41.
• 35. Valis D, Vintr Z, Koucky M. Contribution to highly reliable items' reliability assessment. Reliability, Risk and Safety: Theory and Applications, Proceedings of the European Safety and Reliability Conference, ESREL 2009; Prague, Czech Republic, 2010; 1-3: 1321-1326.
• 36. Yang SK. A condition based failure-prediction and processing-scheme for preventive maintenance. IEEE Transactions on Reliability 2003; 3(52), 373-383.