An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis

An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis

To improve gas-path performance fault pattern recognition for aircraft engines, a new data-driven diagnostic method based on hidden Markov model (HMM) is proposed. A redundant sensor somewhat interferes with fault diagnostic results of the HMM, and it also increases the computational burden. The con...

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Bibliographic Details
Main Authors: Feng Lu, Jipeng Jiang, Jinquan Huang, Xiaojie Qiu
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Energies
Subjects:
gas turbine
fault diagnosis
hidden Markov model
kernel principal component analysis
feature extraction
Online Access:http://www.mdpi.com/1996-1073/11/7/1807

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