WSEAS Transactions on Circuits and Systems
Print ISSN: 1109-2734, E-ISSN: 2224-266X
Volume 24, 2025
Graph Neural Network-Based Motor Fault Classification Model
Authors: , , , ,
Abstract: In this work, we propose a novel motor disorder diagnosis model based on graph neural networks
(GNNs). This model maximizes model performance by incorporating advanced preprocessing techniques such as
Fast Fourier Transform (FFT) and Wavelet Transform (WT). Conventional machine learning and deep learning
models such as CNN and SVM find it difficult to handle nonlinear high-dimensional data in motor disorder
diagnosis. On the other hand, GNN effectively handles these complex data structures, enabling more accurate
and reliable defect classification. Experimental results show that the GNN-based model combining FFT and WT
performed well in the diagnosis of motor disorder. Specifically, the FFT-based GNN showed high accuracy,
accuracy, and reproducibility at an F1 score of 0.95. The GNN model has lower misclassification rate and
higher reliability compared to other models, and ran consistently for various defect types. This is because
GNNs can capture complex relationships within frequency domain function (FFT) and time frequency domain
pattern (WT). For example, rotational imbalance defects are accurately classified thanks to the ability of GNNs
to model harmonic frequency relationships, and bearing defects are accurately classified thanks to the model
sensitivity to local frequency spikes that are effectively represented on nodes and edges of the graph. These
results suggest that GNN-based motor defect diagnostic systems not only improve diagnostic accuracy, but also
have significant potential for real-time applications in manufacturing environments. The system is expected to
reduce maintenance costs and improve operational efficiency. The proposed GNN model makes an important
contribution by providing practical solutions for the detection and prevention of motion disorders.
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Keywords: Motor failure diagnosis , Predictive maintenance , Failure prediction,Graph Neural Networks,FFT,Wavelet Transform
Pages: 92-104
DOI: 10.37394/23201.2025.24.11