Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions
TL;DR
This paper introduces a hybrid CWT-LeNet-5-LSTM model to enhance fault diagnosis in rotating machinery, improving defect detection across different load conditions.
Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions
Muhammad Ahsan; Muhammad Waqar Hassan; Jose Rodriguez; Mohamed Abdelrahem
https://doi.org/10.1109/ACCESS.2024.3522948
Volume 13
The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT...