Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization

Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization

This paper presents a fault diagnosis technique for milling machines based on acoustic emission (AE) signals and a hybrid deep learning model optimized with a genetic algorithm. Mechanical failures in milling machines, particularly in critical components like cutting tools, gears, and bearings, acco...

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Bibliographic Details
Main Authors: Muhammad Umar, Muhammad Farooq Siddique, Niamat Ullah, Jong-Myon Kim
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Applied Sciences
Subjects:
acoustic emission signals
fault diagnosis
condition monitoring
hybrid deep learning model
genetic algorithm
milling machine
Online Access:https://www.mdpi.com/2076-3417/14/22/10404
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