Scientific paper ID 1683 : 2018/3

V. Boyadzhiev

The modeling of the reliable behavior of complete complex technical objects is in principle a difficult problem solving largely due to the considerable diversity of physical factors and processes, which affect the reliability of the objects. This report explores the possibilities for reliable modeling of complex technical systems by building a graph. In particular, the completing system ”Coupling part” of a lathe metal-cutting machine with a digital program control small size, domestic production is considered. Based on the collected statistical information on emerging failures in conditions of real exploitation specific factors and processes that alter the level of operational reliability and operational efficiency of the system under study are identified. A pre-formulated classification of these factors and processes is used for this purpose. On the basis of the actually occurring failures information weighting coefficients of said factors and processes are also determined. The factors and processes that affect the reliability of the complex system under study ”Coupling part” , are divided into three groups:

- conditioned by external factors, including man,

- conditioned by mutual influence of the assembly systems,

- conditioned by factors that are internal to the system.

моделиране надеждност граф металорежеща машинаmodeling reliability graph metalcutting machineV. Boyadzhiev


[1] Konstantinos Pesinis, Kong FahTee, Statistical model and structural reliability analysis for onshore gas transmission pipelines, Engineering Failure Analysis, Volume 82, December 2017, Pages 1-15, 2017, Elsevier Ltd.,

[2] Michele Compare, Luca Bellani, Enrico Zio, Reliability model of a component equipped with PHM capabilities, Reliability Engineering & System Safety, Volume 168, December 2017, Pages 4-11, 2017, Elsevier Ltd.,

[3] P.Wilda, D.Lorenzc, T.Grözingerb, A.Zimmermann, Effect of voids on thermo-mechanical reliability of chip resistor solder joints: Experiment, modelling and simulation, Microelectronics Reliability, Volume 85, June 2018, Pages 163-175, 2018, Elsevier Ltd.,

[4] Fan Wang, Heng Li, Distribution modeling for reliability analysis: Impact of multiple dependences and probability model selection, Applied Mathematical Modelling, Volume 59, July 2018, Pages 483-499, 2018, Elsevier Ltd.,

[5] Ling-ling Li, Cong-Min Lv, Ming-Lang Tseng, Jin Sun, Reliability measure model for electromechanical products under multiple types of uncertainties, Applied Soft Computing, Volume 65, April 2018, Pages 69-78, 2018, Elsevier Ltd.,

[6] Mindaugas Šnipas, Virginijus Radziukynas, Eimutis Valakevičius, Numerical solution of reliability models described by stochastic automata networks, Reliability Engineering & System Safety, Volume 169, January 2018, Pages 570-578, 2018, Elsevier Ltd.,

[7] Peng Weiwen, Narayanaswamy Balakrishnan, Huang Hong-Zhong, Reliability modelling and assessment of a heterogeneously repaired system with partially relevant recurrence data, Applied Mathematical Modelling, Volume 59, July 2018, Pages 696-712, 2018, Elsevier Ltd.




This site uses cookies as they are important to its work.

Accept all cookies
Cookies Policy