Diagnóstico de falhas em turbogerador de plataforma de produção de óleo e gás tipo FPSO

Abstract

This work proposes a diagnostic analysis of the operational data of a turbogenerator mineral lubricating oil system, using a fuzzy inference system (FIS). The study uses real operational data collected from supervisory monitoring sensors in four turbogenerators installed in a Floating Production, Storage and Offloading Unit (FPSO) over a threeyear operational period, resulting in a dataset composed of 40,456,663 input patterns. The failure modes were established through expert knowledge, using the Failure Mode, Effect, and Criticality Analysis (FMECA) documentation as a basis. Initially, the universe variables of the model were constructed using the calibration range of the sensors, and then, fuzzy trapezoidal membership functions were formulated based on the operational limits of each measured parameter. The fault diagnosis model is based on a fuzzy inference system that employs predefined rules derived from expert knowledge, which encapsulates specific fault typologies of the mineral lubricating oil system of turbogenerators, using FMECA as a basis for defining the failure modes and affected parts of this system. The FIS employed as an Artificial Intelligence (AI) tool demonstrates effectiveness in fault diagnosis, with an overall assessment of the system performance that produces satisfactory results, presenting a true positive rate of 98.35% for fault classification, together with a true negative rate of 99.99% for the classification of the normal operating condition of the system. These results highlight the viability of the FIS model for fault diagnosis of the mineral lubricating oil system of turbogenerators, thus showing its potential to improve the operational reliability and maintenance efficiency of these equipments. The model can automate the fault recording process, providing information on fault occurrences and contributing to their recording and diagnosis.