Graph Neural Network-Based Motor Fault Classification Model

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.