Multiscale Spatiotemporal Network With Reinforcement Learning for Predicting Multisensor Systems

To address the modeling challenges posed by multiscale temporal dependencies and sensor spatial correlations in multisensor systems, this article proposes a reinforcement learning-enhanced multiscale spatiotemporal deep network (MSTSDN-RL). Multiscale temporal convolutional networks with causal dilated convolutions are used to capture hierarchical temporal features with enlarged receptive fields, while adaptive graph structure learning and a multibranch graph convolution model capture dynamic sensor correlations. In addition, prediction correction is formulated as a Markov decision process, where reinforcement learning is introduced to sequentially optimize prediction results. Experiments on four C-MAPSS subsets achieve root mean square error (RMSE) values of 12.49, 13.11, 13.58, and 16.37, and Score values of 203.03, 601.25, 205.57, and 1156.88, respectively. On the XJTU-SY dataset, MSTSDN-RL obtains an average RMSE of 29.49 and mean absolute error (MAE) of 25.87. Comparisons with recent state-of-the-art methods, ablation studies, and robustness tests under noise and missing sensor data demonstrate the competitive accuracy, robustness, and generalization capability of the proposed method for aero-engine and rolling bearing prognostics.