Assessment of rolling element bearing degradation based on Dynamic Time Warping, kernel ridge regression and support vector regression-Reference-Cited by-同舟云学术

Assessment of rolling element bearing degradation based on Dynamic Time Warping, kernel ridge regression and support vector regression

Published:2023-06 Issue: Volume:208 Page:109389
ISSN:0003-682X
Container-title:Applied Acoustics
language:en
Short-container-title:Applied Acoustics

Author:

Kumar Prem Shankar,Laha S.K.,Kumaraswamidhas L.A.

Publisher

Elsevier BV

Subject

Acoustics and Ultrasonics

Reference39 articles.

1. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics;Qiu;Adv Eng Inf,2003

2. Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, mel-frequency cepstral coefficients and fractals;Nelwamondo;Int J Innovative Comput Inf Control,2006

3. Frequency response analysis for rolling-bearing damage diagnosis;Zoubek;IEEE Trans Ind Electron,2008

4. Intelligent fault diagnosis of rolling bearing using variational mode extraction and improved one-dimensional convolutional neural network;Ye;Appl Acoustics,2023

5. Bearing early fault detection and degradation tracking based on support tensor data description with feature tensor;Sun;Appl Acoustics,2022

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