Abstract : This research tackles the problem of complicated machine repair systems by proposing a probabilistic model that takes into account incomplete fault coverage and unreliable servers. With imperfect fault coverage, which is represented by probability c, there is a chance that a fault will go unnoticed or unfixed while the system is being repaired. Incorporating a server that isn't reliable increases system uncertainty since the server's reliability affects how well the repair procedure works overall. Using a system of differential equations, the mathematical model captures the transitions between operational, degraded, and failed states with precision. Another aspect that might cause states to transition is when humans make mistakes while fixing things. Examining the effects of server reliability and imperfect fault coverage on system availability, performance, and reliability over time is the primary goal of the study. We may learn more about the machine repair system's dynamic behaviour under various conditions from the simulation findings. In order to determine how well the system handles variations in server reliability and the possibility of incomplete fault coverage, sensitivity assessments are run. If system designers and maintenance professionals want to optimise strategies for making machine repair operations more reliable and available, the results are significant information. Practical applications of machine repair systems involve uncertainties relating to fault coverage and server dependability; this research adds to our understanding of these factors. Integrating probabilistic components into the study allows for a more accurate portrayal of actual machine repair situations, which in turn allows for better decisions about system design and maintenance planning.
Cite : Gupta, D. K., & Phillip, R. (2024). Enhancing Reliability In Machine Repair Systems: A Comprehensive Analysis Of Imperfect Fault Coverage, Human Error And Unreliable Servers (1st ed., pp. 262-273). Noble Science Press. https://doi.org/10.52458/9788197112492.nsp.2024.eb.ch-27
References :
Bhargava C., Jain M. (2014): “Unreliable multiserver queueing system with modified vacation policy”, Opsearch, 51:159–182.
Jain M., Meena R.K. (2018): “Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery”, Journal of Industrial Engineering International,14(1): 143-152.
Jain M., Meena R.K. (2017): “Markovian analysis of unreliable multi-components redundant fault tolerant system with working vacation and F-policy”, Cogent Mathematics, 4: 1306961.
Jain M., Upadhyaya S. (2009). “Threshold N -policy for degraded machining system with multiple type spares and multiple vacations”, Quality Technology and Quantitative Management, 6: 185–203.
Kalaiarasi S., Venkatesan G., Venkatesan E., Nithyapriya N. (2018): “Analysis of imperfect fault coverage reliability of a system”, Journal of Emerging Technologies and Innovative Research, 5(8): 456-461.
Kumar P., Jain M. (2020): “Reliability analysis of a multi-component machining system with service interruption, imperfect coverage, and reboot”, Reliability Engineering & System Safety, 202:106991.
Kuo C.C., Ke J.C. (2015): “Comparative analysis of standby systems with unreliable server and switching failure”, Reliability Engineering and System Safety, 145:74–82.
Sanusi A., Yusuf I. (2023): “Availability and cost benefit analysis of a fault tolerant series–parallel system with human-robotic operators”, Journal of Engineering and Applied Science, 70(71):1-27.
Wang K. H.,Yang D. Y. (2009): “Controlling arrivals for a queueing system with an unreliable server: Newton-Quasi method”, Applied Mathematics and Computation,213: 92–101.
Wu C.H., Yen T.C., Wang K.H. (2021): “Availability and comparison of four retrial systems with imperfect coverage and general repair times”, Reliability Engineering & System Safety, 212:107642.
Yang D. Y.,Wu C. H. (2015): “Cost-minimization analysis of a working vacation queue with N-policy and server breakdowns”, Computers & Industrial Engineering,82: 151–158.
