Training feedforward neural networks for fault diagnosis of ball bearing - Tuan Abdul Rahman, Tuan Ahmad Zahidi; As'arry, Azizan; Abdul Jalil, Nawal Aswan; Raja Ahmad, Raja Mohd Kamil (Centre for Advanced Research on Energy, Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka, 2018)
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.