Enhanced Fault Diagnosis in Milling Machines Using CWT Image Augmentation and Ant Colony Optimized AlexNet

Enhanced Fault Diagnosis in Milling Machines Using CWT Image Augmentation and Ant Colony Optimized AlexNet

A method is proposed for fault classification in milling machines using advanced image processing and machine learning. First, raw data are obtained from real-world industries, representing various fault types (tool, bearing, and gear faults) and normal conditions. These data are converted into two-...

Full description

Bibliographic Details
Main Authors: Niamat Ullah, Muhammad Umar, Jae-Young Kim, Jong-Myon Kim
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Sensors
Subjects:
milling machine
feature optimization
fault diagnosis
modified AlexNet
support vector machine
ant colony optimization
Online Access:https://www.mdpi.com/1424-8220/24/23/7466

Internet

https://www.mdpi.com/1424-8220/24/23/7466

Similar Items