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AUV Fault Diagnosis Based on Multidimensional Temporal Classification Transformer
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DOI：10.2478/pomr-2025-0053.png)
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Abstract
En 中文
Autonomous underwater vehicles (AUVs) play a critical role in marine resource exploration, maritime patrol, and rescue operations, requiring reliable fault diagnosis for robust operation. This paper proposes a novel AUV fault diagnosis method based on the multidimensional temporal classification transformer (MTC-Transformer) deep learning algorithm. The MTC-Transformer enhances traditional transformer architectures by optimising them for multidimensional time-series data processing and fault classification. It features adaptive automatic feature extraction, superior multi-modal data handling, and improved scalability. Key innovations include gated fusion for combining features from outlier-processed data and kernel principal component analysis-reduced time-series data, a dedicated fault feature amplification mechanism, and a multi-layer perceptron head for classification. Trained and validated on the 'Haizhe' small quadrotor AUV fault dataset using double-layer randomised sampling to ensure generalisation, the model achieves exceptional accuracies of 99.51% (training) and 99.44% (validation). Comparative experiments demonstrate its superiority over WDCNN, LSTM-1DCNN, and other benchmarks in handling variable-length sequences and capturing long-range dependencies, confirming its high accuracy, robustness, and practical efficacy for AUV fault diagnosis.
Keywords:
AUV
transformer
fault diagnosis and classification
time-series processing
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