02.09.2026journal-article
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Cross-load robustness evaluation of deep and handcrafted learning frameworks for bearing fault diagnosis
This paper introduces a comprehensive leakage-free methodology for bearing fault diagnosis by three of the most representative feature learning approaches: Raw Signal Convolutional Neural Network (Raw CNN), Fast Fourier Transform (FFT) based hybrid MLP-CNN, and handcrafted statistical descriptor based Multilayer Perceptron (MLP). The vibration data collected from the Case Western Reserve University (CWRU) bearing set for four operating conditions (0, 1, 2, and 3 HP) was used. To eliminate information leakage, all m …