ConTriFormer: triggers-guided contextual informer for remaining useful life prediction of rolling bearings

Abstract Rolling bearings are critical components in many industrial fields, and their stability directly affects the performance and safety of the industrial equipment. Accurate prediction of remaining useful life (RUL) of rolling bearings is a heated topic in modern research. Traditional strategies are unable to efficiently exploit the significant features of the data, resulting in the inability to determine the starting time of prediction along with a reduced prediction accuracy. Accordingly, this paper proposes a novel data-driven prediction model named ConTriFormer, which incorporates multi-feature triggers focusing on various scales of input signals, and the ConvNeXt V2 sparse convolution strategy within the contextual Informer architecture for estimating RUL. Firstly, significant feature indicators of the original data are calculated to construct feature triggers, resulting in a multi-feature fusion. Secondly, the starting time for prediction is obtained through quantified results from fault-sensitive triggers. Thirdly, the original signal with triggers embedded is encoded and organized into sparse matrices to facilitate the simplification of subsequent computations. Sparse features and dynamic context information reflecting bearing state changes are obtained through ConvNeXt V2 sparse convolution, which is input into the Informer structure with contextual attentive structures inside for better adaptability to long time-span dynamic data and lower spatiotemporal complexity for feature mining and prediction. Finally, the prediction results are obtained by mapping output values to the remaining life through a fully connected layer. The proposed algorithm is compared with mainstream deep learning algorithms such as Bi-LSTM and Convolutional Transformer using the XJTU-SY dataset and PHM 2012 dataset, and the effectiveness of model is verified with ablation study. Results show that, the proposed method can more accurately predict RUL, providing a high-precision and intelligent method for prognostics health management of rolling bearings.