Bearings are key components in most rotating machinery, making their reliability crucial for machine performance. Bearing fault detection using vibration analysis has been extensively addressed in the literature. In recent years, advances in machine learning (ML) have significantly contributed to improving and automating the tasks of bearing fault detection. This paper presents a methodology using convolutional neural networks (CNNs) to automatically classify time-frequency representations of vibration signals associated with different bearing faults. These representations are obtained using the short-time Fourier transform (STFT), where several parameters affecting the generation of the images are evaluated. The paper also explores transfer learning to address the issue of limited failure data, using a CNN trained with one dataset to classify bearing faults in a second dataset through fine-tuning techniques. The optimal configurations identified include a fixed number of shaft revolutions instead of fixed time and a local colour normalisation for the STFT images. The proposed model achieves 100% test classification accuracy for both the first dataset and the second dataset after fine-tuning. The results also confirm that transfer learning leads to faster training and model convergence. This methodology significantly improves the reliability and performance of rotating machinery through advanced artificial intelligence (AI) techniques, offering a practical solution for industries facing data scarcity.
Leaman et al. (Sun,) studied this question.