چکیده:
In this research, considering the dynamic nature of systems and machines, an effort has been made to present a model that can consider this feature in the design of the maintenance model. For this purpose, a system dynamics model is presented that considers the most important variables influencing the life cycle, such as system life, mission criticality, access level, improvements in parts and systems, failure rate, etc., in selecting and evaluating the maintenance policy. In fact, we are looking to develop and present a framework that can both evaluate the maintenance policies of the system and simulate the impact of maintenance parameters on the life cycle cost of the system. The criteria used to measure the effectiveness of the maintenance policy used are life cycle cost and cumulative failures. In this research, maintenance modeling has been done using system dynamics knowledge. In fact, the goal of each maintenance model is to create a balance between the costs of failure and lost opportunity with the costs of preventive maintenance, which principle has been used in the proposed model in determining the preventive maintenance policy based on forecasting lost opportunity. The method used is based on predicting future failures using the average cumulative lost opportunities. The presented model specifies the framework for evaluation and decision-making at the three levels of operational management, middle management, and upper levels in order to select an effective maintenance policy.
خلاصه ماشینی:
To this end, a dynamic system model is presented that considers the most influential variables during the life cycle, such as system age, mission criticality, accessibility level, improvements in parts and systems, failure rate, etc.
Research Objectives The purpose of this research is to present a system dynamics model to determine the effectiveness of an appropriate maintenance policy (fixed and variable) considering variables such as system age, maintenance personnel experience, preventive programs, mission criticality, and the level of component/system improvement.
Simulation of results for an average failure rate Suitable selection lcc Sum BD Sum PM Sum OL PM1 0 76 110 1563 5492 1 69 110 1413 5318 2 73 110 1501 5436 4 , 1 3 80 110 1643 5577 4 70 110 1426 5305 5 78 111 1587 5531 Where: PM1: Number of preventive maintenance in the first year Sum BD: Cumulative number of failures Sum PM: Cumulative number of preventive maintenance actions Sum OL: Cumulative number of lost opportunities LCC: Life cycle cost 34 Providing a model to determine an effective maintenance policy with ...
Figure 5 shows the results of the variable policy without considering the life factor (the effect of time on failures is zero) Life cycle cost Cumulative failures Preventive maintenance activity per year 1 0 830 38987 10 831 39054 20 863 39997 30 796 38313 40 822 39201 50 843 39767 60 838 39345 70 834 39409 80 814 39168 90 793 38475 100 769 37839 Figure 6.