Bayesian-Tuned Convolutional Neural Networks for Precise Bearing Fault Classification

This paper presents a solution for the detection of bearing outer and inner race faults using a Convolutional Neural Network (CNN) that directly analyzes raw data. Our proposed approach takes advantage of deep learning to capture intricate patterns indicative of fault conditions. To further optimize the performance of the CNN, we integrate Bayesian optimization to systematically search for the most effective network hyperparameters. The study includes a comprehensive comparison with a baseline solution and an alternative CNN configuration that analyzes spectrograms. Through rigorous experimentation and evaluation on benchmark datasets, we demonstrate the efficacy of the proposed Bayesian optimized CNN to achieve superior accuracy and robustness in detecting bearing faults. The results highlight the potential of integrating Bayesian optimization techniques to fine-tune CNN hyperparameters, enhancing the model's ability to generalize and adapt to diverse fault scenarios. This research contributes to the advancement of fault detection methodologies, particularly in the domain of bearing health monitoring, showcasing the effectiveness of deep learning coupled with hyperparameter optimization.