MATHEMATIC SIMULATION OF SERIES CONFIGURED SYSTEM UNDER PREVENTIVE AND CORRECTIVE MAINTENANCE USING PSO

For any industry looking to optimize earnings while incurring minimal maintenance costs, unbroken operating time has become crucial. However, the continuous strain wears out the system's components, which ultimately leads to a reduction in system efficiency and complete failure. Total failure is not always controllable; it could lead to a large loss of revenue or output. In this sense, even with reduced efficiency, preventative maintenance is essential to making sure the sector functions properly. In any industry, preventive maintenance is necessary to meet the demands of high productivity at low cost and maximum profit. This paper addressed the performance of a well-maintained two-unit system by calculating reliability metrics like the average duration of system failure and the available time to perform it by counting the effect of the collapse rate and its reparation facilities. Any machine's output is entirely dependent on its inputs and processing, and achieving increasingly higher profits depends critically on the accuracy of the results. The goal of this paper is to maximize the overall profit using a nature inspired optimization tool, particle swarm optimization by counting the reliability metrics for the performance of a series framework subjected to the influence of routine upkeep and changing of components using the Markov process and Chapman-Kolmogorov differential equations. This study focused on two operational units with preventive maintenance features for one crucial unit. Corrective maintenance is applied by most of the authors, but the application of preventive maintenance reduces the cost of repairing and also the cost of changing parts of a system. Throughout the operation of the system, a variation in the rates of malfunction and repair has been observed. The average time to system failure is counted with preventive maintenance rates and availability is checked out in the effect of both preventive and corrective maintenance rates. For reliability metrics across various rates, the results have been calculated. The most appropriate potential values across all variables that follow the exponential distribution have been examined and explored in order to increase reliability and boost businesses. According to the findings, maintaining the system well is essential to raising its overall efficiency. Here is a solution and graphic representation for a mathematical instance.