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

• Main Authors: Ong Pauline, Ong Pauline, Tan Yean Keong, Tan Yean Keong, Lai Kee Huong, Lai Kee Huong, Sia Chee Kiong, Sia Chee Kiong
• Format: Article
• Language: English
• Published: Elsevier 2023
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/11486/1/J15990_2064fc6358db37f4a127145f5fa98b10.pdf; http://eprints.uthm.edu.my/11486/; https://doi.org/10.1016/j.dajour.2023.100219

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.