Fuzzy Inference System Development for Turbogenerator Failure Diagnosis on Floating Production Offloading and Storage Platform

Patricio F. Castro (), George Rossany Soares de Lira, Pablo Bezerra Vilar, Edson G. da Costa and Fabricio B. S. Carvalho
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Patricio F. Castro: Petrobras, Av. Henrique Valadares, 28, Centro Empresarial Senado, Bairro, Centro, Rio de Janeiro CEP 20.231-030, RJ, Brazil
George Rossany Soares de Lira: Post-Graduation Program in Electrical Engineering, Department of Electrical Engineering, Campina Grande Federal University (UFCG), Campina Grande CEP 58.429-900, PB, Brazil
Pablo Bezerra Vilar: Post-Graduation Program in Electrical Engineering, Department of Electrical Engineering, Campina Grande Federal University (UFCG), Campina Grande CEP 58.429-900, PB, Brazil
Edson G. da Costa: Post-Graduation Program in Electrical Engineering, Department of Electrical Engineering, Campina Grande Federal University (UFCG), Campina Grande CEP 58.429-900, PB, Brazil
Fabricio B. S. Carvalho: Post-Graduation Program in Electrical Engineering, Department of Electrical Engineering, Federal University of Paraíba (UFPB), João Pessoa CEP 58.051-900, PB, Brazil

Energies, 2024, vol. 17, issue 2, 1-16

Abstract: This paper introduces a novel approach for diagnosing failures within a turbogenerator mineral lube oil system, employing a fuzzy inference system (FIS) model. The study leverages real operational data collected from supervisory monitoring sensors across four turbogenerators over a three-year operational span, resulting in a dataset comprising 40,456,663 input parameters. The failure modes were established through expert knowledge, using the Failure Mode, Effect, and Criticality Analysis (FMECA) documentation as the basis. Initially, the model’s universe variables were constructed using the sensor calibration range, and then the fuzzy membership functions were formulated based on the operational thresholds inherent to each measured parameter. The fault identification mechanism is underpinned by an inference system employing predefined rules, extrapolated from expert judgments encapsulating failure typologies specific to the turbogenerators’ mineral lube oil system, as delineated in the FMECA. The FIS model demonstrates notable efficacy in failure diagnosis with an overall performance evaluation of the system yielding satisfactory outcomes, having a 98.35% true positive rate for failure classification, coupled with a 99.99% true negative rate for accurate classification during normal system operation. These results highlight the visibility of the FIS model in diagnosing failures within the turbogenerator mineral lube oil system, thereby showcasing its potential for enhancing operational reliability and maintenance efficiency.

Keywords: fuzzy inference system; failure diagnosis; turbogenerator; failure mode, effect, and criticality analysis (FMECA) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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