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Fault Diagnosis of UUV Motor Based on Two-stream CNN-LSTM Model
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Xueqian CHEN1, 2, Junge SHEN1, 2, Junqiang BAI1, 2, Haosheng TAN1, 2, Haoran HUANG1, 2
Missiles and Space Vehicles | 2025, 48(4) : 59 - 66
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Missiles and Space Vehicles | 2025, 48(4): 59-66
• Artificial Intelligence Technology •
Fault Diagnosis of UUV Motor Based on Two-stream CNN-LSTM Model
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Xueqian CHEN1, 2, Junge SHEN1, 2, Junqiang BAI1, 2, Haosheng TAN1, 2, Haoran HUANG1, 2
Affiliations
  • 1. Unmanned System Research Institute, Northwestern Polytechnical University, Xi′an, 710072
  • 2. National Key Laboratory of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi′an, 710072
Published: 2025-08-25 doi: 10.7654/j.issn.2097-1974.20250408
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To address the limitations of existing methods for underwater unmanned vehicle (UUV) motor fault diagnosis, which rely on manual feature extraction and do not fully leverage the potential of intelligent diagnosis, a two-stream CNN-LSTM fault diagnosis model is proposed. The model employs convolutional neural networks as feature extractor, which can learn the low frequency trend and high frequency detail features of the original signal without complex pre-processing steps, making real-time motor status monitoring possible. Afterwards, the classifier based on the long short-term memory network uses these features to explore temporal dependencies and identify motor faults. Experiments are conducted on a self-constructed UUV motor fault simulation platform, and the performance of the model is validated by setting multiple speeds and load conditions. The results show that this method can efficiently diagnose six typical states in UUV motors and achieve an average diagnostic accuracy of 97.22%. These findings demonstrate the model's effectiveness and robustness in UUV motor fault diagnosis.

underwater unmanned vehicle  /  motor  /  artificial intelligence  /  fault diagnosis  /  convolutional neural networks  /  long short-term memory
Xueqian CHEN, Junge SHEN, Junqiang BAI, Haosheng TAN, Haoran HUANG. Fault Diagnosis of UUV Motor Based on Two-stream CNN-LSTM Model[J]. Missiles and Space Vehicles, 2025 , 48 (4) : 59 -66 . DOI: 10.7654/j.issn.2097-1974.20250408
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Year 2025 volume 48 Issue 4
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Article Info
doi: 10.7654/j.issn.2097-1974.20250408
  • Receive Date：2024-10-25
  • Online Date：2025-10-27
  • Published：2025-08-25
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  • Received：2024-10-25
  • Revised：2025-02-14
Affiliations
    1. Unmanned System Research Institute, Northwestern Polytechnical University, Xi′an, 710072
    2. National Key Laboratory of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi′an, 710072
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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