SCKAN: A Lightweight Model-free Fault Diagnosis Method for Autonomous Underwater Vehicles

This paper presents a novel approach to fault diagnosis in Autonomous Underwater Vehicles (AUVs) using a Sequence Convolutional Kolmogorov-Arnold Network (SCKAN). The proposed method addresses the critical challenge of achieving high-accuracy fault detection while maintaining a lightweight model suitable for power-limited AUV applications. SCKAN combines the strengths of Sequence Convolutional Neural Networks (SeqCNN) for processing sequential data and Kolmogorov-Arnold Networks (KAN) for efficient function representation. We evaluate our method using the "Haizhe" AUV dataset, considering five common fault types. The SCKAN achieves an average classification accuracy of 96.8%, comparable to state-of-the-art methods. Notably, it reduces the parameter count by 78.2% compared to the best-performing SeqCNN model, with only a 1.1% decrease in accuracy. This significant reduction in model complexity makes SCKAN particularly suitable for real-time, on-board fault diagnosis in AUVs. The proposed method opens up new possibilities for efficient, accurate, and real-time fault diagnosis in AUVs, potentially improving their safety and extending their operational capabilities in challenging underwater environments.