Accurate fault diagnosis in rotating machinery is critical for predictive maintenance and operational reliability in industrial applications. Despite the effectiveness of deep learning, many models underperform due to manually selected hyperparameters, which can lead to premature convergence, overfitting, weak generalization, and inconsistent performance across binary and multi-class classification. To address these limitations, the study proposes a novel hybrid hyperparameter optimization framework that combines Robotic Brain Storm Optimization (RBSO) with Manta Ray Foraging Optimization (MRFO) to optimally fine-tune deep learning architectures, including MLP, LSTM, GRU-TCN, CNN-BiLSTM, and Transformer-LSTM models. The framework leverages RBSO for global search to promote diversity and prevent premature convergence, and MRFO for local search to enhance convergence toward optimal solutions, with their combined effect improving predictive model performance and methodological generalization. The approach was validated on three benchmark datasets, including Case Western Reserve University (CWRU), industrial machine fault detection (TMFD), and the Machinery Fault Dataset (MaFaulDa). Before optimization, Transformer-LSTM model achieved 98.35% and 97.21% accuracy on CWRU binary and multi-class classification, 99.52% and 98.57% on TMFD, and 98.18% and 92.82% on MaFaulDa. Following hybrid optimization, Transformer-LSTM exhibited superior performance, with accuracies increasing to 99.72% for both CWRU tasks, 99.97% for TMFD, and 99.98% and 98.60% for MaFaulDa, substantially reducing misclassification. These results demonstrate that the proposed RBSO–MRFO framework provides a scalable, robust, and high-accuracy solution for intelligent fault diagnosis in rotating machinery.
Ali et al. (Tue,) studied this question.