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Utilizing Principal Component Analysis for the Identification of Gas Turbine Defects

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Abstract

This study explores the use of the nonlinear principal component analysis (NLPCA) technique for detecting gas turbine faults. The resurgence of interest in neural network-based NLPCA in recent years has propelled its application in fault diagnosis. In this context, a five-layer neural network NLPCA approach is employed to model normal gas turbine operation without faults. The fault detection relies on the filtered squared prediction error index. The research involves determining the optimal count of retained principal components within the PCA model and validating this model by comparing actual and estimated variables. The efficacy and dependability of this approach in gas turbine defect detection are demonstrated, contrasting it with the linear principal component analysis approach. Real gas turbine operational data spanning four years is utilized for this comparison. The findings underscore the superior efficiency of the chosen NLPCA approach, suggesting its value as a robust tool for fault monitoring and diagnosis in gas turbines.

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  1. Badji Mokhtar University, Annaba, Algeria

    Fenghour Nadir, Bouakkaz Messaoud & Hadjadj Elias

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  1. Fenghour Nadir
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  2. Bouakkaz Messaoud
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Correspondence to Fenghour Nadir.

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Nadir, F., Messaoud, B. & Elias, H. Utilizing Principal Component Analysis for the Identification of Gas Turbine Defects. J Fail. Anal. and Preven. 24, 97–107 (2024). https://doi.org/10.1007/s11668-023-01817-2

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  • DOI: https://doi.org/10.1007/s11668-023-01817-2

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