A Few‐Shot Fault Localization Agent Model for AUV Propeller Systems Based on Knowledge‐Data Hybrid Drive

ABSTRACT Aiming at the problem of locating the faulty bearing of robot propeller, the traditional mechanism modeling method is difficult to establish and cannot cover all unknown factors due to too many factors involved; however, models that rely on actual data are difficult to provide enough training samples due to expensive equipment and complex working conditions. Therefore, an agent model based on knowledge and data fusion is proposed to accurately capture the mapping relationship between input data and fault‐bearing location. First, the mechanism model of the propeller‐bearing rotor system under ideal working conditions is established, and data samples are generated. Then, a fault location agent model‐building method based on incremental knowledge distillation is proposed, which integrates mechanism knowledge with actual data. Combined with the data continuously obtained in actual operation, the agent model is continuously updated and optimized to enable it to dynamically adjust under different working conditions and improve the fault location accuracy. Finally, the fault feature attribute description strategy is embedded in the agent model to make the representation of mechanism knowledge and data more consistent, so as to achieve more effective integration of the two. The experimental results show that the agent model not only significantly reduces the complexity of building the model, but also can accurately reflect the mapping relationship between data and fault‐bearing location under different working conditions through only one measuring point, so as to achieve accurate diagnosis of fault‐bearing location.