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This is a Experimental Study study.

September 29, 2025The International Journal of Acoustics and VibrationOpen Access

283A Robust Rolling Bearing Fault Diagnosis Approach Using Hybrid Red Deer Optimization-Based 1D-CNN

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Authors

TJThomas JosephA P J Abdul Kalam Technological University USU. SudeepA P J Abdul Kalam Technological University KKKeerthi Krishnan KDr Panjabrao Deshmukh Krishi Vidyapeeth

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Implication

This research demonstrates enhanced fault classification accuracy in rolling bearings using variational mode decomposition and hybrid red deer algorithm with 1D-CNN.

Key Points

The proposed method achieved maximum fault classification accuracy of 99.7% with the CWRU dataset.
Noise in vibration signals is reduced using a wavelet threshold denoising method before feature extraction.
Features are extracted using variational mode decomposition and multifractal detrended fluctuation analysis.
The classification is performed using an optimized 1D convolutional neural network integrated with the hybrid red deer algorithm.

Cite This Study

Joseph et al. (2025) studied this question.

synapsesocial.com/papers/68da58dcc1728099cfd112dc https://doi.org/10.20855/ijav.2025.30.32128

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Implication

This research demonstrates enhanced fault classification accuracy in rolling bearings using variational mode decomposition and hybrid red deer algorithm with 1D-CNN.

283A Robust Rolling Bearing Fault Diagnosis Approach Using Hybrid Red Deer Optimization-Based 1D-CNN

Discussion

Member takes

Implication

Key Points

Cite This Study

283A Robust Rolling Bearing Fault Diagnosis Approach Using Hybrid Red Deer Optimization-Based 1D-CNN

Discussion

Member takes

Implication

Key Points

Cite This Study

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