Jian Liu
ABSTRACT
As modern manufacturing systems developed towards larger size, higher automation, more flexible, more efficient and higher precision continuously, the parts and equipment in systems greatly increase. As a result, the probability of system failures will be increased significantly under complex operating environment. Therefore, how to ensure the reliability of manufacturing systems has become a major problem facing the users of manufacturing systems. In order to solve this problem, a reliability analysis method of multi-state manufacturing system (MSMS) under specific operating environment was proposed in this paper. Firstly, the definition of operating environment of MSMS is stated and three aspects factors (machining process, human personnel and environmental condition) affecting the operational reliability are explained. Secondly, the state space model was introduced to describe the state degradation path of manufacturing system and its main equipment units with specific operating factors. Thirdly, an improved universal generating function (UGF) method is employed to analyze the performance state and operational reliability of MSMS. Finally, a case study of manufacturing system for machining double-parts cluster composed of cylinder is taken to prove the validity and usability of this method. It is very significant to enhance reliability and capability of manufacturing equipment by this method.
- A. Lisnianski and G. Levitin (2003). Multi-state system reliability: assessment, optimization and applications. World Scientific, Singapore.Google Scholar
Cross Ref
- R. Barlow and A. Wu (1978). Coherent systems with multistate elements. Mathematics of Operations Research, (3), 275-281.Google Scholar
- S. Ross (1979). Multivalued state element systems. Annals of Probability, (7), 379-383.Google Scholar
- B. Natvig and A. Streller (1984). The steady-state behavior of multistate monotone systems. Journal of Applied Probability, 21(4), 826-835.Google ScholarCross Ref
- T. Aven and U. Jensen (1999). Stochastic models in reliability. Springer-Verlag, New York.Google ScholarCross Ref
- J. Xue and K. Yang (1995). Dynamic reliability analysis of coherent multistate systems. IEEE Transactions on Reliability, 44(4), 683-688.Google ScholarCross Ref
- R.D. Brunelle and K.C. Kapur (1999). Review and classification of reliability measures for multi-state and continuum models. IIE Transactions, 31(12), 1171-1180.Google ScholarCross Ref
- A. Lisnianski and Y. Ding (2009). Redundancy analysis for repairable multi-state system by using combined stochastic processes methods and universal generating function technique. Reliability Engineering and System Safety, 94(11), 1788-1795.Google ScholarCross Ref
- I. Ushakov (1986). Universal generating function. Soviet Journal of Computer and Systems Sciences, 24(5), 118-129.Google Scholar
- A. Lisnianski, G. Levitin, H. Ben-Haim, (1996). Power system structure optimization subject to reliability constraints. Electric Power Systems Research, 39(2), 145-152.Google ScholarCross Ref
- G. Levitin and A. Lisnianski (1999) Optimal multistage modernization of power system subject to reliability and capacity requirements. Electric Power Systems Research, 50(3), 183-190.Google ScholarCross Ref
- G. Levitin, D.X. Liu, H. Ben-Haim, (2011) Multi-state systems with selective propagated failures and imperfect individual and group protections. Reliability Engineering and System Safety, 96(12), 1657-1666.Google ScholarCross Ref
- R. Billinton and W.Y. Li (1991) Hybrid approach for reliability evaluation of composite generation and transmission systems using Monte-Carlo simulation and enumeration technique. IEE Proceedings C: Generation, Transmission and Distribution, 138(3), 233-241.Google ScholarCross Ref
- E. Zio, M. Marella and L. Podofillini (2007) A Monte Carlo simulation approach to the availability assessment of multi-state systems with operational dependencies. Reliability Engineering and System Safety, 92(7), 871-882.Google ScholarCross Ref
- J.E. Ramirez-Marquez and D.W. Coit. (2005) A Monte-Carlo simulation approach for approximating multi-state two-terminal reliability. Reliability Engineering and System Safety, 87(2), 253-264.Google ScholarCross Ref
- E. Zio, L. Podofillini and G. Levitin (2004) Estimation of the importance measures of multi-state elements by Monte Carlo simulation. Reliability Engineering and System Safety, 86(3), 191-204.Google ScholarCross Ref
- M.J. Zuo and Z.G. Tian (2006) Performance evaluation for generalized multi-state k-out-of-n systems. IEEE Transactions on Reliability, 55(2), 319-327.Google ScholarCross Ref
- W. Li and M.J. Zuo (2008) Reliability evaluation of multi-state weighted k-out-of-n systems. Reliability Engineering and System Safety, 93(1), 160-167.Google ScholarCross Ref
- V.K. Sharma, M. Agarwal and K. Sen (2011) Reliability evaluation and optimal design in heterogeneous multi-state series-parallel systems. Information Sciences, 181(2), 362-378.Google ScholarDigital Library
- G. Levitin and D.X Liu (2010) Reliability and performance of multi-state systems with propagated failures having selective effect. Reliability Engineering and System Safety, 95(6), 655-661.Google ScholarCross Ref
- A.M.A. Youssef, A. Mohib and H.A. ElMaraghy (2006) Availability assessment of multi-state manufacturing systems using universal generating function. CIRP Annals, 55(1), 445-448.Google ScholarCross Ref
- A.M.A. Youssef and H.A. ElMaraghy (2008) Performance analysis of manufacturing systems composed of modular machines using the universal generating function. Journal of Manufacturing Systems, 27(2), 55-69.Google ScholarCross Ref
- Y. Ding and A. Lisnianski (2008) Fuzzy universal generating functions for multi-state system reliability assessment. Fuzzy Sets and Systems, 159(3), 307-324.Google ScholarDigital Library
- Y. Liu, H.Z. Huang and G. Levitin (2008) Reliability and performance assessment for fuzzy multi-state elements. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 222(4): 675-686.Google ScholarCross Ref
- K.T. Huynh, A. Barros, C. Bérenguer, (2011) A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events. Reliability Engineering and System Safety, 96(4), 497-508.Google ScholarCross Ref
- K.T. Huynh, A. Barros and C. Bérenguer (2012) Adaptive condition-based maintenance decision framework for deteriorating systems operating under variable environment and uncertain condition monitoring. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 226(6), 602-623.Google ScholarCross Ref
- D. Chen and C. Zhao (2009) Data-driven fuzzy clustering based on maximum entropy principle and PSO. Expert Systems with Applications, 36(1), 625-633.Google ScholarDigital Library
Recommendations
Multi-state system in human reliability analysis
HSI'09: Proceedings of the 2nd conference on Human System InteractionsApplication of mathematical model of Multi-State System for human reliability analysis is considered in the paper. This paper describes new method for estimation of changes of more than one component states and they influence to Multi-State System ...
Manufacturing Execution System for a Subsidiary of Aerospace Manufacturing Industry
ICCAE '09: Proceedings of the 2009 International Conference on Computer and Automation EngineeringAbstract—Aerospace manufacturing requires precision tools and control systems. Integrated Manufacturing Execution System (MES) in a subsidiary of aerospace manufacturing industry initiate, guide, respond to and report on manufacturing activities as they ...
An empirical exploratory study on operating system reliability
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied ComputingHigh-reliability applications run on top of commodity operating systems, which hence must provide high reliable services. In this paper, we conduct an empirical exploratory study on OS reliability. We analyze more than 30,000 real OS failure data ...
Comments