Research on Underdriven HOV Fault Diagnosis based on Physical Attention-Multidimensional Recurrent Neural Networks

Abstract

Abstract In recent years, underdriven human occupied vehicles (HOVs) have gained immense popularity due to their exceptional maneuverability and efficiency. However, the intricate nature of their propulsion systems, coupled with the harsh underwater environment, presents substantial challenges in terms of fault diagnosis. To address this issue, this study proposes a fault diagnosis approach that leverages the Physical Attention-Multidimensional Recurrent Neural Networks (PA-MDRNN) for underdriven HOV propulsion systems. The PA-MDRNN model is trained using a dataset comprising both normal and faulty HOV propulsion systems. Subsequently, the trained model is utilized for real-time identification and diagnosis of faults. Distinguishing itself from conventional data-driven models, our approach introduces a novel non-consistent attention mechanism based on fundamental computational physical properties. This mechanism operates within the framework of hybrid networks, where attention is computed on the multidimensional mapping results. This computation enhances the focus on desired features that adhere to traditional physical properties, thereby eliminating any training results that contradict established physical laws. Consequently, this approach not only excludes incongruous training outcomes but also reinforces the adherence to physical properties in the finalized training results.