Open‐Set Fault Diagnosis for Autonomous Underwater Vehicles Via Prototype Learning and Adaptive Mahalanobis Gating

ABSTRACT Reliable fault diagnosis is essential for the safe operation of autonomous underwater vehicles (AUVs) in uncertain and dynamic environments. However, conventional closed‐set diagnosis methods are unable to handle previously unseen fault conditions, while existing open‐set techniques often struggle with low‐frequency, highly coupled multivariate telemetry. This paper proposes ProtoNet‐MDAG, a prototype‐based open‐set diagnosis framework for AUV fault recognition. Specifically, a dilated multi‐scale encoder is developed to extract discriminative temporal features from low‐frequency sensor streams, and a variance‐penalized prototypical learning strategy is introduced to enforce compact and well‐structured known‐class manifolds in the latent space. Based on this representation, an adaptive Mahalanobis distance gating mechanism is constructed to perform statistically calibrated open‐set rejection. Experimental results on a Haizhe AUV and a BlueROV‐class underwater robotic platform show that the proposed method achieves 97.19% and 89.12% accuracy, respectively. The results demonstrate strong unknown‐state rejection on the Haizhe AUV and competitive known‐class recognition on the BlueROV‐class platform, while also revealing that platform‐dependent distribution shift can weaken unknown‐state rejection in more heterogeneous operating conditions. Robustness tests on the Haizhe dataset further show that the framework remains stable under representative telemetry perturbations. These results demonstrate that the proposed framework provides an effective and reliable solution for open‐set fault diagnosis in underwater robotic systems.