Rolling bearing fault diagnosis under variable speed and noisy conditions using multidomain ConvNeXt features and random forest decisions

Reliable rolling-bearing diagnosis requires features and decision rules that remain discriminative under operating-speed variation and severe interference. This study presents a staged TL-ConvNeXt-RF framework in which 1024-point vibration windows are represented by spatially aligned time-domain, fast Fourier transform amplitude, and Hilbert–Huang marginal-spectrum channels. An ImageNet-initialized ConvNeXt-Tiny learns 768-dimensional multidomain features, and a validation-optimized random forest performs ensemble classification. The framework achieved 97.95–99.32% accuracy in same-speed diagnosis, 93.47% in direct 1200-to-1800 r/min evaluation, and 96.35% after supervised target-speed RF adaptation. In the principal speed-stratified experiment, the − 4 dB curves show that RF retained approximately 82% and 75% accuracy at 1200 and 1800 r/min, respectively, compared with approximately 65% and 60% for Softmax; in direct cross-speed testing, the corresponding RF and Softmax values were approximately 62% and 47%. External validation on the CWRU benchmark yielded 98.83–99.87% clean test accuracy and showed substantially slower low-SNR degradation for RF. A distinct pooled-condition protocol retained 94.18% clean accuracy for both decision heads and yielded modest RF gains under impulsive and pink noise. Because this protocol differs from the speed-stratified experiment in condition pooling, trained checkpoint, and RF fitting, its absolute accuracies are interpreted separately. Protocol-specific analyses additionally evaluate the multidomain input and transfer initialization. The results support RF ensemble decisions as a useful noise-stability mechanism for multidomain ConvNeXt features under the evaluated conditions.