UTHM Institutional Repository

Development of indigenous method for analysing bearing performance using artificial neural network

Mahamad, Abd Kadir and Saon, Sharifah and Yahya, Musli Nizam and Ghazali, Mohd Imran (2006) Development of indigenous method for analysing bearing performance using artificial neural network. In: 1st Malaysian Technical University Colleges, Annual Conference On Engineering and Technology, 19 - 20 December 2006 , Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor.

Full text not available from this repository.


Due to close relationship between machines performance and bearings, there are difficulties to imagine modern rotating machineries without considering the wide applications of bearings. At present, most of bearings failure detection performed by expert supervisors either through sight or touch. This approach susceptible to human error and varies according to experience and individual skills. In this paper an indigenous approach was taken to solve this problem. The bearings failure data were measured based on the acoustic emission (AE) and these data were measured in term of decibel (dB) and Distress level. The data were then used to develop the model using artificial neural network (ANN) for bearing fault prediction. An experimental rig was setup to collect data on bearing by using Machine Health Checker (MHC) Memo assist with MHC Analysis software. In the development of ANN modeling, the result showed that the optimum model was Elman network with training algorithm, Levenberg-Marquardt Back-propagation. In the application part, a computer program was written on bearing failure prediction. This program was implemented using graphical user interface (GUI) features that can be implemented by using a MATLAB GUI. Any user can use this program as a tool to operate or simulate bearing failure prediction.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:34
Last Modified: 13 Aug 2018 03:34
URI: http://eprints.uthm.edu.my/id/eprint/9649
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item