Journal Article

Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

Arunachalam Sundaram, Abdullahi Abubakar Mas’ud, Adewusi, Surajuddeen, Ahmad, Jamal

2021

Overview

In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.

• Publisher: IAES Institute of Advanced Engineering and Science
• Subject: Artificial neural networks / Blades / Classification / Fault detection / Laboratory tests / Neural networks / Rotating machinery / Rotating machines / Rotors / Test equipment / Training / Vibration measurement