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

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

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Authors

Abdullahi Abubakar Mas’ud

Ahmad Jamal

Surajuddeen Adewusi



Abstract

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.

Citation

Abubakar Mas’ud, A., Jamal, A., Adewusi, S., & Sundaram, A. (2021). Rotating blade faults classification of a rotor-disk-blade system using artificial neural network. International Journal of Power Electronics and Drive Systems, 12(3), 1900-1911. https://doi.org/10.11591/ijpeds.v12.i3.pp1900-1911

Journal Article Type Article
Acceptance Date Jul 17, 2021
Publication Date 2021
Deposit Date Jul 23, 2024
Publicly Available Date Jul 26, 2024
Journal International Journal of Power Electronics and Drive Systems
Publisher Institute of Advanced Engineering and Science
Peer Reviewed Peer Reviewed
Volume 12
Issue 3
Pages 1900-1911
Series ISSN 2088-8694
DOI https://doi.org/10.11591/ijpeds.v12.i3.pp1900-1911
Keywords Artificial Neural Network; Rotating Machines