<ul><li>Fault diagnosis is critical for ensuring the stability and reliability of train transmission systems.</li><li>Data-driven fault diagnosis models offer advantages over traditional methods, but existing models are limited in their ability to handle compound faults.</li><li>A new approach using a frequency domain representation and a 1-dimensional CNN is proposed for compound fault diagnosis in train transmission systems.</li><li>The proposed model achieved accuracies of 97.67% and 93.93% on test sets for single and compound faults, respectively.</li></ul>

Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation

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