The impact of random parameter distribution on RVFL model performance in bearing fault diagnosis

While deep learning has made significant progress in many applications including fault diagnosis, its relatively high computational cost and long training time seriously limits its applicability in some areas. To address these challenges, lightweight neural networks, such as the randomly weighted networks like the random vector functional link (RVFL) with a non-iterative training mechanism, have been proposed. In the RVFL model, the initialization of weights plays a crucial role in determining model performance. Therefore, this paper investigates the impact of different random parameter distributions on RVFL model performance in bearing fault diagnosis. Specifically, we propose a weight generation strategy that approximately follows uniform or normal distributions, and through a case study, we compare the effects of these distributions on the model. Subsequently, we conduct an experimental analysis on a publicly available bearing anomaly detection dataset. The experimental results demonstrate that the choice of distribution affects the model’s accuracy, with the normal distribution showing slightly better performance than the uniform distribution in this application scenario. These findings provide some guidelines for selecting appropriate parameter distributions for bearing fault diagnosis using RVFL networks.