A Novel Fault Diagnosis Method Based on NEEEMD-RUSLP Feature Selection and BTLSTSVM

A Novel Fault Diagnosis Method Based on NEEEMD-RUSLP Feature Selection and BTLSTSVM

The vibration signal of rolling bearings is a nonlinear and non-stationary signal, which is affected by the working condition change and background noise, and the reliability of traditional feature extraction methods and fault identification methods is low. In order to effectively extract feature ve...

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Bibliographic Details
Main Authors: Rongrong Lu, Miao Xu, Chengjiang Zhou, Zhaodong Zhang, Shanyou He, Qihua Yang, Min Mao, Jingzong Yang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Empirical mode decomposition
unsupervised feature selection
mixed domain
fault diagnosis
least square twin support vector machine
Online Access:https://ieeexplore.ieee.org/document/10283816/

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