Engine Wear Fault Diagnosis Based on Supervised Kernel Entropy Component Analysis

Authors

Zhichao Zhu, Engineering Research Center of internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Dinghui Wu, Engineering Research Center of internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Yuanchang Yue, Engineering Research Center of internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Focus on the influence of environment on engine operation, which leads to a large amount of redundant information and nonlinear structure in oil spectral data that affects the engine fault diagnosis results, the feature extraction method of SKECA (supervised kernel entropy component analysis) is proposed. A supervised learning algorithm is adopted on the basis of Kernel Entropy Component Analysis, which extracts the inherent geometric features of oil spectrum data to make the extracted fault features include the discriminative information. GA (genetic algorithm) is used to find parameters to optimize the results of feature extraction, and SVM (support vector machine) is used to classify the fault features. Simulation results show that SKECA can effectively improve the accuracy of engine fault diagnosis.

Recommended Citation

Zhu, Zhichao; Wu, Dinghui; and Yue, Yuanchang (2022) "Engine Wear Fault Diagnosis Based on Supervised Kernel Entropy Component Analysis," Journal of System Simulation: Vol. 34: Iss. 1, Article 5.
DOI: 10.16182/j.issn1004731x.joss.20-0623
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss1/5

First Page

45

Revised Date

2020-11-24

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0623

Last Page

52

CLC

TP391.9

Recommended Citation

Zhu Zhichao, Wu Dinghui, Yue Yuanchang. Engine Wear Fault Diagnosis Based on Supervised Kernel Entropy Component Analysis[J]. Journal of System Simulation, 2022, 34(1): 45-52.

DOI

10.16182/j.issn1004731x.joss.20-0623