زمان‌بندی نگه‌داری و تعمیرات پیشگیرانه برای سیستم‌های سری، موازی، و جایگزینی تک‌قطعه براساس تحلیل‌های آماری

Abstract

Preventive maintenance scheduling of generating units is addressed as a crucial issue in power system studies due to its severe impacts on power systems' asset management by reducing operation cost while enhancing reliability worth. In this paper, we describe an application of statistical analysis for determining the best preventive maintenance strategy in the case of parallel, series, and single-item replacement systems. A key aspect of industrial maintenance is the trade-off between cost and time of performing preventive maintenance operations. This article deals with two main issues related to the preventive maintenance in order to minimize the cost per unit time: (١) when is the best time for performing preventive maintenance actions in parallel, series and single-item replacement systems and (٢) How many spare parts and facilities are required in single-item replacement and parallel systems respectively. In this proposed maintenance strategy, preventive maintenance operations are regularly performed on the production unit in equal time intervals and maintenance actions return the facilities to a good-as-new condition. In the proposed model, the number of failures for a specified facility follows the Poisson distribution and failure rate of the Poisson distribution follows Gamma process. In addition an equipment' failure earlier than the determined starting time for the preventive maintenance operations will impose too much cost to each system. So to consider this constraint, it is assumed that there is a linear relation between recovering cost from unplanned failures and time to failure. Also all costs related to maintenance are supposed to be known and the cost of a preventive repair is appreciably less than the cost of a failure and its associated repair. Finally, three examples are presented to demonstrate the effectiveness of the proposed models for each system. We use MATLAB software to obtain optimal value of parameters in cost models in the proposed approach. To find the optimal value of decision variables in each system, a search procedure is applied.