LSTP-Net: a lightweight spatiotemporal fusion framework for probabilistic prediction of aviation bearing under complex operational conditions with interpretability

Abstract Deep learning not only enables end-to-end learning of fault features and adaptive feature extraction, but also deeply uncovers the inherent degradation patterns embedded in the data, demonstrating great potential in the field of remaining useful life prediction for rotating machinery. However, the degradation behavior of aviation bearings exhibits high uncertainty. Existing models often face challenges such as difficulty in extracting degradation features, high computational complexity, and poor interpretability. To address the aforementioned challenges, this paper proposes a lightweight spatiotemporal fusion framework for probabilistic prediction of aviation bearings, named the LSTP-Net, which is referred to as the Lightweight Spatio-Temporal Probabilistic Network. This study designs 3 core modules that work in synergy to construct a high-precision and robust aviation bearing life prediction model. First, a lightweight spatial feature extraction module is designed to efficiently capture local dependencies and spatial patterns from vibration signals. Subsequently, the extracted local degradation features are fed into a lightweight temporal feature extraction module. This module employs global sequence modeling to learn and embed the aforementioned spatial features into temporal information, thereby comprehensively extracting the spatiotemporal features that characterize performance degradation. Finally, the probability assessment module characterizes the uncertain information within the degradation data with interval estimates, quantifying the uncertainty in predictions. The experimental results indicate that, compared with 5 mainstream methods such as Squeeze-Bi-GRU-GPR-Net, the proposed model demonstrates significant advantages in terms of interval quality, and computational efficiency.