A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features

A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features

Gearbox failures can lead to substantial damage, significant financial losses due to maintenance downtimes, and, in some instances, fatalities. This study introduces an intelligent gear fault diagnosis system employing a convolutional neural network (CNN), utilizing vibration and thermal features ex...

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Bibliographic Details
Main Authors: Pauline Ong, Pauline Ong, Anelka John Koshy, Anelka John Koshy, Kee Huong Lai, Kee Huong Lai, Chee Kiong Sia, Chee Kiong Sia, Maznan Ismon, Maznan Ismon
Format: Article
Language:English
Published: Elsevier 2024
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.uthm.edu.my/10963/1/J17474_6913d9be8e815071bc4a9ed648d52d56.pdf
Description
Summary:Gearbox failures can lead to substantial damage, significant financial losses due to maintenance downtimes, and, in some instances, fatalities. This study introduces an intelligent gear fault diagnosis system employing a convolutional neural network (CNN), utilizing vibration and thermal features extracted from healthy, chipped, and broken tooth gear health categories. The performance of the CNN is compared with conventional machine learning models, including Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) classifiers. Experimental investigations highlight CNN’s remarkable performance. With vibration features, the CNN achieved 96.78% accuracy, surpassing SVM (84.89%), NB (81.56%), and RF (85.11%). The CNN attained 100% accuracy when utilizing thermal features, while SVM, NB, and RF achieved 91.11%, 88.89%, and 96.51% accuracies, respectively.

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