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Keywords: principal component analysis
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Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. April 2025, 147(4): 041016.
Paper No: GTP-24-1246
Published Online: November 5, 2024
... reducing the required sample size. The uncertainties in the rotor and stator blade rows of a one-stage compressor are investigated to verify this method. Using principal component analysis and machine learning, the projection amplitudes of the interface aerodynamic flow field onto the principal modes...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Eng. Gas Turbines Power. April 2012, 134(4): 042501.
Published Online: January 25, 2012
... multiple accelerometers and microphones are then fused together through a statistical weighting approach based on principal component analysis. Using the highlighted and fused features, we demonstrate that different gear faults can be effectively detected and identified. 05 05 2011 09 05 2011...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Eng. Gas Turbines Power. March 2012, 134(3): 032901.
Published Online: December 29, 2011
...Jianping Ma; Jin Jiang In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification of the instruments in nuclear power plants. A KPCA model for fault isolation and identification is proposed by using the average sensor reconstruction errors. Based...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Eng. Gas Turbines Power. December 2011, 133(12): 122502.
Published Online: August 26, 2011
... geometry changing effects. The sensitivity coefficients make use of a set of principal component analysis (PCA) modes that describe the measured blade geometry variation. Once the sensitivity coefficients are determined, they are used to construct the IC matrices and to predict the aerodynamic damping...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Eng. Gas Turbines Power. July 2008, 130(4): 041602.
Published Online: April 29, 2008
... accuracy? The classifiers studied in this paper are the support vector machine, probabilistic neural network, k -nearest neighbor, principal component analysis, Gaussian mixture models, and a physics-based single fault isolator. As these algorithms operate on large volumes of data and are generally...