Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine

Keywords: Principal Component Analysis, Fault Detection, Fault Diagnosis, Internal Combustion Engine

Abstract

Based on increasing global energy demand, electric power generation from Internal Combustion Engines (ICE) has increased over the years. On this idea, the industries have adopted different methods and procedures to prevent failures in these engines, achieve an extension of the life cycle of the machines, improve their safety, and provide financial savings. For this reason, this work implements a methodology for detecting and identifying failures in a natural gas engine (JGS 612 GS-N. L), based on the integration of Principal Component Analysis (PCA) and alarm streak analysis.

A method used to describe a data set in terms of new uncorrelated variables or components. The components are ordered by the amount of original variance they describe, making the technique useful for reducing the dimensionality of a data set.

Technically, PCA searches for the projection according to which the data are best represented in terms of least squares, using the T2 and Q statistics. In the initial stage, a PCA-based algorithm was developed to detect abnormal process trends and identify the variables of greater impact when these anomalies arise. In the next stage, an algorithm was developed and implemented, based on the analysis of alarm streaks, to identify the system’s behavior and thus classify fluctuations into either normal operating condition drifts or system failures. The application of the proposed methodology with real operation data of the engine (JGS 612 GS-N. L) shows that the method outperforms operators in detecting and identifying faults, as it performs these tasks considerably earlier than operators.

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Author Biographies

Yulineth Cardenas, Universidad de la Costa (CUC)

Department of Energy

Grupo de Investigacion GIOPEN

Gaylord Carrillo, Universidad Tecnologica de Bolívar (UTB)

Research Group on Alternative Energies and Fluids (EOLITO)

Anibal Alviz, Universidad Señor de Sipan

Grupo de Investigacion en Deterioro de Materiales

Transición Energética y Ciencia de datos DANT3

Arquitectura y Urbanismo

Antistio Alviz, Universidad de Cartagena

Facultad de ciencias Farmaceutica

Grupo de Farmacologia y Terapeutica

Ivan Portnoy, Universidad de la Costa (CUC)

Department of Productivity and Innovation Department

Juan Fajardo, Universidad Tecnologica de Bolivar (UTB)

Research Group on Alternative Energies and Fluids (EOLITO)

Eric Ocampo, Federal University of Itajuba – UNIFEI

Excellence Center in Renewable Energy and Energy Efficiency – EXCEN

Edson Da-Costa, Federal University of Itajuba – UNIFEI

Excellence Center in Renewable Energy and Energy Efficiency – EXCEN

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Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine

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Published
2022-11-29
How to Cite
Cardenas, Y., Carrillo, G., Alviz, A., Alviz, A., Portnoy, I., Fajardo, J., Ocampo, E., & Da-Costa, E. (2022). Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine. EUREKA: Physics and Engineering, (6), 84-98. https://doi.org/10.21303/2461-4262.2022.002701
Section
Engineering