A Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic Graph LSTSVM

A Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic Graph LSTSVM

Rolling bearings, as critical components of rotating machinery, significantly influence equipment reliability and operational efficiency. Accurate fault diagnosis is therefore crucial for maintaining industrial production safety and continuity. This paper presents a new fault diagnosis method based...

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Bibliographic Details
Main Authors: Rongrong Lu, Miao Xu, Chengjiang Zhou, Zhaodong Zhang, Kairong Tan, Yuhuan Sun, Yuran Wang, Min Mao
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Entropy
Subjects:
fast complementary ensemble empirical mode decomposition
fault feature selection
feature extraction
directed acyclic graph least squares twin support vector machine
fault diagnosis
Online Access:https://www.mdpi.com/1099-4300/26/12/1031
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