Plan de mantenimiento aplicando la metodología (RCM) para el área de producción de Cepolfi Industrial C.A.

Abstract

The main objective of this research was to propose a maintenance plan applying the RCM methodology for the production area of the company Cepolfi Industrial C.A. An analysis and coding of the equipment and machinery in the production area of the company was carried out, which was made up of 3 digits, the first corresponding to the production plant, the second digit to the area where the equipment is located, and the third digit belonged to the initials and numbering of each of the machines, following the guidelines of the ISO 14224 standard. Subsequently, failure modes and effects matrices (FMEA) were made, in which the components of each machinery and equipment are broken down, with the respective failures that occur, after which the IRP (Risk Priority Index) was calculated by multiplying the severity index (G), by the occurrence index (O) and the detectability (D), which according to NTP 679, if the value is greater than 100 the risk is considered high. Similarly, the decision matrices of each machinery of the Cepolfi company were created based on the weighted factor method, considering 5 factors, the priority for the operation of the machine (P), the implementation cost (C), the technical feasibility (F), the scope (A), and the available resources (R), each of these factors were scored as 1 = Low, 2 = Medium Low, 3 = Medium, 4 = Medium High and 5 = High, this helped in the selection of the most relevant maintenance tasks for each equipment and machinery. With the previously developed procedures, the predictive maintenance plan based on RCM was developed, focused on the identification of key components, fault prioritization, definition of specific protocols, selection of tasks based on decision matrices. Finally, the maintenance schedule was included with a one-year projection, using color scales with their respective frequency of performance, which becomes a tool to organize, plan and optimize the management of predictive maintenance, with the purpose of increasing production, preventing unforeseen stoppages.