The advent of intelligent fault diagnosis has advanced condition monitoring in industrial machinery. This study develops and evaluates two lightweight convolutional neural networks (CNNs): a 1-D CNN for time-domain vibration signals and a 2-D CNN for continuous wavelet transform (CWT)–based scalogram images in rolling-element bearing fault diagnosis. A key feature of this work is its dual-modality framework that independently examines temporal and time–frequency representations to assess diagnostic accuracy and computational efficiency. Using the Case Western Reserve University (CWRU) dataset and an in-house experimental dataset, both models demonstrate strong generalization under variable conditions. The 1-D CNN achieves 99.67% mean accuracy with significantly lower computation time and memory usage than AlexNet and GoogLeNet, while the 2-D CNN attains 98.74% accuracy with improved interpretability. Performance remains stable across 10 independent runs, with 95% confidence intervals confirming the robustness of both models for future edge-oriented fault diagnosis applications.
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