Intelligent fault diagnosis of turbine blade cracks via multiscale sparse filtering and multi-kernel support vector machine for information fusion-Reference-Cited by-同舟云学术

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Intelligent fault diagnosis of turbine blade cracks via multiscale sparse filtering and multi-kernel support vector machine for information fusion

Published:2023-04 Issue: Volume:56 Page:101979
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Huang Xin,Zhang Xiaodong,Xiong Yiwei,dai fei,Zhang Yingjie

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems,Building and Construction

Reference61 articles.

1. Creep/fatigue accelerated failure of Ni-based superalloy turbine blade: Microscopic characteristics and void migration mechanism;Han;Int. J. Fatigue,2022

2. Analysis and prevention for ablation failure of turbine blade in aero-engine;Yao;Fail. Anal. Prev.,2006

3. Strain energy gradient-based LCF life prediction of turbine discs using critical distance concept;Zhu;Int. J. Fatigue,2018

4. Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition;Hoseinzadeh;ISA Trans.,2018

5. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis with Model-Based and Signal-Based Approaches;Gao;IEEE Trans. Ind. Electron.,2015

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