PSTFormer: A novel parallel spatial-temporal transformer for remaining useful life prediction of aeroengine

One of the significant tasks in aeroengine remaining useful life (RUL) prediction is to address both temporal dependencies and spatial dependencies in multivariate time series (MTS) monitoring data. However, it is difficult for traditional transformer-based methods simultaneously extract both temporal and spatial dependencies due to their mutual interference, limiting the further improvement of prediction performance. To address these issues, this paper proposes a novel Parallel Spatial-Temporal Transformer (PSTFormer) for aeroengine RUL prediction with multi-sensor monitoring data. First, a novel parallel spatial-temporal attention mechanism (PSTAM) is designed, which consists of a temporal attention module (TAM) and a spatial attention module (SAM), to simultaneously capture temporal and spatial dependencies from MTS data. TAM employs multiscale convolution to learn the temporal dependencies at different time scales, while SAM adopts a self-attention mechanism to learn the spatial dependencies among different sensor parameter. Parallel connection between TAM and SAM can effectively avoid the mutual interference between temporal and spatial dependencies, improving the modeling ability of complex spatiotemporal relationships. Second, a task-guided spatiotemporal feature fusion (TG-STFF) module is designed, which adaptively fuses temporal and spatial features according to downstream task. Specifically, based on the RUL prediction characteristic, TG-STFF converts spatial features into attention weights and fuses them with temporal features to extract more representative degradation features. Finally, the effectiveness of PSTFormer is validated by a series of experimental comparisons on the public C-MAPSS dataset. Compared with SOTA methods, PSTFormer exhibits more outstanding prediction performance, and it can effectively address the aforementioned challenges in RUL prediction tasks. Therfore, the development of PSTFormer provides an innovative and effective method for aeroengine RUL prediction, significantly enhancing the efficiency and safety of aeroengine maintenance and operation.