Training feedforward neural networks for fault diagnosis of ball bearing

Citation

Tuan Abdul Rahman, Tuan Ahmad Zahidi and As'arry, Azizan and Abdul Jalil, Nawal Aswan and Raja Ahmad, Raja Mohd Kamil (2018) Training feedforward neural networks for fault diagnosis of ball bearing. In: 5th Mechanical Engineering Research Day (MERD'18), 3 May 2018, Kampus Teknologi UTeM, Melaka. (pp. 290-292).

Abstract

Vibration-based condition monitoring plays important roles for early fault detection and diagnosis of expensive rotating machinery. This paper presents the application of a novel metaheuristic approach named chaos-enhanced stochastic fractal search (CFS) to train feedforward neural networks (FNNs) for monitoring a ball bearings system. The vibration response data are analyzed using statistical methods to characterize several defects of ball bearings and generate vibration signature features. Then, a novel CFS-based FNNs approach is applied to classify these ball bearings conditions. The results show that the proposed approach produces comparable classification accuracy as parameters of the FNNs were optimized systematically using CFS algorithm.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Engineering
Publisher:	Centre for Advanced Research on Energy, Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka
Keywords:	Artificial neural networks; Damage classification; Stochastic fractal search
Depositing User:	Nabilah Mustapa
Date Deposited:	03 Sep 2018 04:54
Last Modified:	03 Sep 2018 04:54
URI:	http://psasir.upm.edu.my/id/eprint/65091
Statistic Details:	View Download Statistic

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