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

Pauline Ong, Pauline Ong and Anelka John Koshy, Anelka John Koshy and Kee Huong Lai, Kee Huong Lai and Chee Kiong Sia, Chee Kiong Sia and Maznan Ismon, Maznan Ismon (2024) A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features. Decision Analytics Journal, 10. pp. 1-9.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network Gearbox Machine learning Fault diagnosis Thermal image Vibrational signal
Subjects:	T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Mechanical and Manufacturing Engineering
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	15 May 2024 07:17
Last Modified:	15 May 2024 07:17
URI:	http://eprints.uthm.edu.my/id/eprint/10963

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