Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions

Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions

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 ac...

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Bibliographic Details
Main Authors: Muhammad Ahsan, Muhammad Waqar Hassan, Jose Rodriguez, Mohamed Abdelrahem
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Continuous wavelet transform (CWT)
LeNet-5
long short-term memory (LSTM)
fault diagnosis
vibration analysis
predictive maintenance
Online Access:https://ieeexplore.ieee.org/document/10816403/
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