Temporal-Spatial Attention Network: A Novel Axial Piston Pump Coupled Fault Diagnosis Method

Multisource fault patterns usually occur simultaneously in the axial piston pump. The coupled fault problem makes different fault patterns affect each other on the failure mechanism, leading to the aggravation of each fault pattern. Meanwhile, the features of different fault patterns will be aliased on the monitored signals, making some relatively weak fault patterns hard to diagnose. In this article, a novel framework named temporal-spatial attention network (TSAN) is proposed to solve this problem. The key of the proposed method is to effectively extract fault-sensitive features from multisource sensors on both temporal and spatial scales. First, to extract periodic fault-sensitive features of each sensor on the temporal scale, the multihead attention-based temporal feature extraction model is constructed. Then, to fuse the extracted temporal features of each sensor and extract fault-sensitive features between different sensors on a spatial scale, the multihead attention-based spatial feature extraction model is constructed. The proposed learnable temporal feature token and spatial feature tokens effectively transmit the temporal features and spatial features and improve the efficiency of the fault-sensitive temporal-spatial feature extraction. Compared with other state-of-the-art methods, comparison experiments demonstrate that the proposed framework improves the fault diagnosis accuracy under coupled fault problems by at least 22.06%.