Abstract
In industrial settings, long-time series data often exhibit high noise levels and complex structures, presenting significant challenges for accurate prediction and maintenance. To address these issues, this paper proposes temporal fusion network (TFN), a novel data fusion algorithm designed for processing industrial time series data. TFN integrates variational mode decomposition (VMD) for denoising, reconstruction, and gap-filling with a hybrid neural network architecture. This architecture combines a temporal convolutional network (TCN) for capturing hierarchical patterns and a gated recurrent unit (GRU) for modeling long-term dependencies. This approach effectively mitigates the influence of high noise and overcomes the limitations of deep convolutional neural network (DCNN) algorithms in handling long-term dependencies. The effectiveness of TFN is demonstrated through experiments on real-world datasets for Industrial Component Degradation Prediction and Predictive Maintenance of Industrial Motors, showcasing its potential for enhancing predictive capabilities in industrial applications.
This paper was recommended by Regional Editor Tongquan Wei
