Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture

Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture

ABSTRACT Artificial intelligence offers a promising solution for the precise identification of faults in rotating machinery. The severe repercussions of turbomachinery blade failures, including fatalities and extensive damage, necessitate robust diagnostic tools. Early fault detection and diagnosis...

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Bibliographic Details
Main Authors: Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Engineering Reports
Subjects:
1D convolutional neural network
automatic feature extraction
blade faults
fault classification
hybrid deep learning
traditional fault diagnosis
Online Access:https://doi.org/10.1002/eng2.70202
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https://doi.org/10.1002/eng2.70202

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