Temporal convolutional and fusional transformer model with Bi-LSTM encoder-decoder for multi-time-window remaining useful life prediction

TL;DR

This paper introduces a novel deep learning framework combining TCNs, a modified Transformer with Bi-LSTM, and multi-time-window analysis to improve multi-time-window RUL prediction accuracy in industrial systems.

Contribution

It presents a new integrated model that captures both short- and long-term dependencies and adapts across operating conditions for more accurate RUL prediction.

Findings

Reduces average RMSE by up to 5.5% on benchmark datasets

Effectively captures temporal dependencies and salient patterns

Enhances robustness across diverse operating conditions

Abstract

Health prediction is crucial for ensuring reliability, minimizing downtime, and optimizing maintenance in industrial systems. Remaining Useful Life (RUL) prediction is a key component of this process; however, many existing models struggle to capture fine-grained temporal dependencies while dynamically prioritizing critical features across time for robust prognostics. To address these challenges, we propose a novel framework that integrates Temporal Convolutional Networks (TCNs) for localized temporal feature extraction with a modified Temporal Fusion Transformer (TFT) enhanced by Bi-LSTM encoder-decoder. This architecture effectively bridges short- and long-term dependencies while emphasizing salient temporal patterns. Furthermore, the incorporation of a multi-time-window methodology improves adaptability across diverse operating conditions. Extensive evaluations on benchmark datasets demonstrate that the proposed model reduces the average RMSE by up to 5.5%, underscoring its improved predictive accuracy compared to state-of-the-art methods. By closing critical gaps in current approaches, this framework advances the effectiveness of industrial prognostic systems and highlights the potential of advanced time-series transformers for RUL prediction.