Aplicação do FMEA para gestão do conhecimento de manutenção mecânica de equipamentos em uma indústria de papel

Abstract

This research defined the development of fault tree analysis as its theme. The delimitation consists of presenting a tool for capturing FMEA maintenance knowledge in the mechanical maintenance sector of the company ALFA, as a knowledge management tool, having as a history for the future the equipment failure modes and their respective causes. The investigation problem focused on the fact that employees who carry out maintenance do not have improved and qualified knowledge, showing little ability to perceive failures and, mainly with respect to the quality of maintenance knowledge and little experience in analyzing problems. The need to create a maintenance knowledge management plan was verified, and in the knowledge capture stage, FMEA was used to structure a failure perception pattern for the analyzed equipment, as well as to manage the technical knowledge acquired during plant operating time. The main objective was to demonstrate the application of the FMEA tool for the most common centrifugal pump sets in the paper machine area. Specifically, it sought to present a literature review on FMEA, describe the pump sets, demonstrate the FMEA application phases, and produce considerations on the application of a maintenance knowledge management program. The research had its justification in the sense of reliability in the production process to act in the detection of failures in the mechanical maintenance of equipment. The methodological procedures defined the exploratory research and the case study carried out with equipment maintenance employees from the paper industry selected for the research, with a deductive method of analysis. From the results, the development of a failure mode and effect analysis table contributed to the maintenance knowledge management system at different stages, including the identification of assets and systems that require maintenance; to capture knowledge; tool for learning new maintainers; knowledge base for solving future problems; inclusion of probabilistic risk analysis techniques; and, classification and organization of knowledge about asset failures.