A deep convolutional neural network for vibration-based health-monitoring of rotating machinery

Ong Pauline, Ong Pauline and Tan Yean Keong, Tan Yean Keong and Lai Kee Huong, Lai Kee Huong and Sia Chee Kiong, Sia Chee Kiong (2023) A deep convolutional neural network for vibration-based health-monitoring of rotating machinery. Decision Analytics Journal, 7. pp. 1-9.

Text J15990_2064fc6358db37f4a127145f5fa98b10.pdf Restricted to Registered users only Download (1MB) | Request a copy

Abstract

The gearbox is a critical component in the mechanical system, requiring vigilant monitoring to prevent adverse consequences on safety and quality due to malfunction. Therefore, early fault diagnosis of the gearbox before the fatal breakdown of the entire mechanical system is of imperative importance. This study proposes a onedimensional deep convolutional neural network (1D-DCNN) to learn features directly from the vibrational signals and identify the gear fault under different health conditions. The performance is compared with the decision tree, random forest, and support vector machine to validate the superiority of the 1D-DCNN. Experimental results showed that the proposed scheme outperforms other comparative methods, with a diagnostic accuracy of 97.11 %, thus confirming its effectiveness.

Item Type:	Article
Uncontrolled Keywords:	Deep convolutional neural network Gearbox Fault diagnosis Vibrational signal Wavelet packet decomposition
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Mechanical and Manufacturing Engineering
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	17 Jul 2023 07:40
Last Modified:	17 Jul 2023 07:40
URI:	http://eprints.uthm.edu.my/id/eprint/9212

Actions (login required)

View Item