Reliability Analysis of Multi-State Engine Units Utilizing Time-Domain Response Data
Yongfeng FANG1, Wenliang TAO1, Kong Fah TEE2
1. School of Electronic & Mechanical Engineering, Guizhou University of Science Technology, Bijie 551700, China;
2. Department of Engineering Science, University of Greenwich, Kent ME44 TB, United Kingdom
A novel reliability-based approach has been developed for multi-state engine systems. Firstly, the output power of the engine is discretized and modeled as a discrete-state continuoustime Markov random process. Secondly, the multi-state Markov model is established. According to the observed data, the transition intensity is determined. Thirdly, the proposed method is extended to compute the forced outage rate and the expected engine capacity deficiency based on time response. The proposed method can therefore be used for forecasting and monitoring the reliability of the multi-state engine utilizing time-domain response data. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.
Supported in part by the National Natural Science Foundation of China (61473331), the Foundation from the Excellent Researcher of Bijie University (G2013017, G2015003), the Science and Technology Foundation of Guizhou (2001), and the Project of Guizhou Province Experiment Demonstration Teaching Center
Cite this article:
Yongfeng FANG,Wenliang TAO,Kong Fah TEE. Reliability Analysis of Multi-State Engine Units Utilizing Time-Domain Response Data[J]. Journal of Systems Science and Information, 2016, 4(4): 354-364.
Fang Y, Chen J, Tee K F. Analysis of structural dynamic reliability based on the probability density evolution method. Structural Engineering and Mechanics, 2013, 45(2):201-209.
Natvig B. Multi-state systems reliability theory with application. New York:John Wiley & Sons, 2011.
Lisnianski A, Levitin G. Multi-state system reliability:Assessment, optimization, applications. World Scientific Press, 2003.
Billinton R, Allan R N. Reliability evaluation of power systems. New York:Plenum Press, 1996.
Manoukas G E, Athanatopoulou A M, Avramidis I E. Multimode pushover analysis based on energy equivalent SDOF systems. Structural Engineering and Mechanics, 2014, 51(4):531-546.
Barlow R E, Wu A S. Coherent system with multi-state components. Mathematics of Operations Research, 1978, 3(1):275-281.
Ross S. Multi-valued state component systems. The Annals of Probability, 1979, 7(2):379-383.
Fang Y, Wen L, Tee K F. Reliability analysis of repairable k-out-n system from time response under several times stochastic shocks. Smart Structures and Systems, 2014, 14(4):559-567.
Tee K F, Khan L R. Reliability analysis of underground pipelines with correlation between failure modes and random variables. Journal of Risk and Reliability, Proceedings of the Institution of Mechanical Engineers, Part O, 2014, 228(4):362-370.
Billinton R, Gao Y, Huang D, et al. Adequacy assessment of wind-integrated composite generation and transmission systems. Innovations in Power Systems Reliability, Springer Series in Reliability Engineering, London:Springer, 2011.
Reshid M, Abd Majid M. A multi-state reliability model for gas fueled cogenerated power plant. Journal of Applied Science, 2011, 11(11):1945-1951.
Vosooq A K, Zahrai S M. Study of an innovative two-stage control system:Chevron knee bracing & shear panel in series connection. Structural Engineering and Mechanics, 2013, 47(6):881-898.
Aven T. On performance measures for multi-state monotone systems. Reliability Engineering and System Safety, 1993, 41(3):259-266.
Brunelle R D, Kapur K C. Review and classification of reliability measures for multi-state and continuum models. Transactions of Institute of Industrial Engineers, 1999, 31(2):1171-1181.
Limnios N, Oprian G. Semi-Markov processes and reliability in statistics for industry and technology. Boston:Birkhauser, 2001.
Goldner S. Markov model for a typical 360 MW coal fired generation unit. Communication in Dependability and Quality Management, 2006, 9(1):9-24.
Jahanshahia M R, Rahgozar R. Free vibration analysis of combined system with variable cross section in tall buildings. Structural Engineering and Mechanics, 2012, 42(5):715-728.
Barbu V, Boussemart M, Limnios N. Discrete time semi-Markov processes for reliability and survival analysis. Communications in Statistic, Theory and Methods, 2004, 33(11):2833-2868.
Koroliuk V S, Limnios N, Samoilenko I. Poisson approximation of impulsive recurrent process with semi-Markov switching. J. Stochastic Analysis and Applications, 2011, 29(5):769-778.
Menshikova M, Petritis D. Explosion, implosion, and moments of passage times for continuous-time Markov chains:A semimartingale approach. Stochastic Processes and Their Applications, 2014, 124(7):2388-2414.
Barbu V, Limnios N. Semi-Markov chain and hidden semi-Markov models toward applications in lecture notes in statistic. Berlin:Springer, 2008.
Janssen J, Manca R. Semi-Markov risk models for finance, insurance and reliability. Berlin, Germany:Springer-Verlag, 2007.
Lisnianski A, Elmakias D, Laredo D, et al. A multi-state Markov model for a short-term reliability analysis of a power generating unit. Reliability Engineering and System Safety, 2012, 98(3):1-6.