Search

In this paper, we present a general formula to calculate transition probabilities for six different types of systems based on their redundancy strategy and the status of the components. The systems are under periodic inspection policy and their components are repairable. The investigated systems include system I – active redundancy without any component to be replaced or repaired; system II – active with the component(s) to be replaced and repaired; system III – active with the component(s) to be repaired; system IV – standby without any component(s) to be replaced or repaired; system V – standby with the component(s) to be replaced and repaired; and system VI – standby with the component(s) to be repaired. In addition, all components in a system are considered non-identical. To calculate the transition probabilities for systems IV, V, and VI, we first consider a system with n non-identical components and cold standby configuration (NIC-CSC) and calculate the system state probabilities using Markov theory. Then, we present the general formula transition probabilities for systems IV, V, and VI using the results of the NIC-CSC system inspection interval. We demonstrate how to calculate the transition probabilities and matrixes for the six above-mentioned systems using the provided formulas. Moreover, we use the provided formulas to optimize the inspection interval of these systems using a modified full enumeration technique. [ABSTRACT FROM AUTHOR]

Estimating the reliability and maintainability (R & M) parameters is crucial in various industrial applications. It serves purposes such as evaluating system performance and safety, minimising the risk and cost of potential failures, and designing efficient maintenance strategies. This task becomes challenging for complex repairable systems, where failures can occur due to different causes and performance may be affected by various covariates (such as material, environment, and labour). Another challenge in R & M studies arises from the presence of censorship in failure times. Existing methodologies often fail to account for all the aforementioned aspects of system‐related data in R & M analysis. By incorporating valuable information from covariates and utilising data from censored failure times alongside complete failure data, the accuracy of R & M parameter estimation can be significantly improved. This paper develops reliability models for repairable systems with multiple failure causes in the presence of covariates. The system can also be subject to imperfect maintenance. The R & M parameters are then estimated by applying the Kijima Type I and II model's virtual age concept. The proposed technique is illustrated using two case studies on gas pipelines and aero‐engine systems. Through these case studies, we show that the proposed method not only provides more efficient estimates of the R & M parameters compared to the alternative approach, but it is also easier to apply and yields more straightforward interpretations. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the effects of production disruptions (PDs) and physical distancing constraints due to the pandemic in a parallel-machine production environment. The machines are non-identical and are utilized for producing a finite set of jobs (parts) in a plastic injection moulding production. The production process is subjected to random production downtime disruptions. A mixed-integer linear programming (MILP) model is developed for optimizing the joint production plan and schedule, which maximizes the production's total benefit. The model is utilized to plan and schedule a set of 17 machines in a Canadian manufacturing company. To explore the effects of physical distancing and PDs on the production's total net profit, four different scenarios for normal operation and production during the pandemic, with and without production downtimes, are considered. A genetic algorithm is utilized to solve the model. The results show that considering machines' random breakdowns and physical distancing individually reduces the total profit of the production by 71.58 and 57.98%, respectively; while their joint effect results in a 88.54% reduction in the annual net profit. [ABSTRACT FROM AUTHOR]