Bayesian Neural Network-Based Equipment Operational Trend Prediction Method Using Channel Attention Mechanism - Chang Ming-Yu; Tian Le; Maozu Guo (IEEE Access, 2024)
This paper proposes a Bayesian neural network method for predicting equipment operational trends based on a channel attention mechanism. Traditional time series prediction methods have limitations in handling complex data and nonlinear relationships. To enhance prediction accuracy and stability, the paper introduces a channel attention mechanism to capture crucial features and contextual information within the data. This mechanism automatically adjusts the weights of feature channels to focus on the influence of key features. By leveraging the advantages of Bayesian neural networks, the model undergoes multiple updates and adjustments while considering uncertainty factors, progressively improving the predictive outcomes. In experiments, the paper utilizes power transformer data from a Kaggle public dataset and a substantial amount of temporary facility equipment data from the Winter Olympics site, comparing the performance against other commonly used prediction methods. Results demonstrate the significant superiority of the Bayesian neural network method with channel attention mechanism in equipment trend prediction, outperforming traditional time series models and other commonly used methods.