CLASSIFICATION OF BEARING FAULTS USING EXTREME LEARNING MACHINE ALGORITHMS

Roller element bearing fault diagnosis is crucial for industry to maintain machine in good condition so that there is no delay of work due to machine breaks down. This project implements the bearing fault diagnosis that classifies the bearing data into four classes which are healthy bearing, inner race defect bearing, outer race defect bearing, and roller element defect bearing. Most of existing bearing fault diagnosis are done using Back Propagation (BP) algorithm which take a long time to train the neural network resulting in inefficiency of training the Single Hidden Layer Feedforward Neural Network (SLFN). Therefore, this project introduces three learning algorithms which are Extreme Learning Machine (ELM), Finite Impulse Response Extreme Learning Machine (FIR-ELM) and Discrete Fourier Transform Extreme Learning Machine (DFT-ELM) to improve the bearing fault diagnosis accuracy and shorten the time used to train and test the neural network