Remaining Useful Life Prediction Based on Multisensor Fusion and Attention TCN-BiGRU Model

Abstract

Predicting remaining useful life (RUL) accurately is crucial for improving the reliability of the digitized maintenance and optimizing the operating cycle of devices. Based on the attention mechanism, this article proposes a temporal convolutional neural network (TCN)-bidirectional gate recurrent unit (BiGRU) model for the RUL prediction. The proposed model can accurately predict the RUL by fusing the time-series information recorded by multiple sensors. To suppress noise signals, the sensor’s raw signal filtered by the contribution rate is processed by variational mode decomposition (VMD) and decomposed into intrinsic mode components (IMFs) with different modes. Then, grid search and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -fold cross validation are performed to determine the optimal hyperparameters of the proposed Attention TCN-BiGRU model to accurately capture the correlation of IMFs in the spatial dimension and the decay characteristics in the temporal dimension. The performance of the proposed model is validated on the commercial modular aero-propulsion system simulation (C-MAPSS) turbofan engine dataset and the sealing ring dataset. The experimental results indicate that the proposed model can achieve more accurate RUL predictions than other related models.