| 水下机器人推进系统自适应故障诊断 |
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| 引用本文： | 徐高飞,王晓辉,赵洋. 水下机器人推进系统自适应故障诊断[J]. 舰船科学技术, 2020, 42(6): 95-100. DOI: 10.3404/j.issn.1672-7649.2020.06.019 |
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| 作者姓名： | 徐高飞 王晓辉 赵洋 |
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| 作者单位： | 中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016;中国科学院机器人与智能制造创新研究院,辽宁沈阳110016;中国科学院大学,北京100049;中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016;中国科学院机器人与智能制造创新研究院,辽宁沈阳110016 |
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| 基金项目： | 海洋公益性行业科研专项;国家重点研发计划 |
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| 摘 要： | 针对水下机器人推进系统的在线监测,提出一种具有在线学习能力的推进系统故障诊断方法。通过分析相关性的变化趋势,在线估计推进系统的时延。利用作业过程中采集的数据,对控制量与转速之间的关系进行在线建模。为提高建模精度,采用粒子群算法,对模型阶次和建模数据量进行在线优化。为适应作业过程中环境和系统自身状态的变化,设计了模型在线更新机制。基于该在线更新机制,提出一种不依赖传统阈值的自适应故障检测方法。通过海上试验数据和水池测试,验证了所提出算法的有效性。
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| 关 键 词： | 水下机器人 推进系统 自适应故障诊断 时延估计 |
Adaptive fault diagnosis for thruster system of underwater vehicles |
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| XU Gao-fei,WANG Xiao-hui,ZHAO Yang. Adaptive fault diagnosis for thruster system of underwater vehicles[J]. Ship Science and Technology, 2020, 42(6): 95-100. DOI: 10.3404/j.issn.1672-7649.2020.06.019 |
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| Authors: | XU Gao-fei WANG Xiao-hui ZHAO Yang |
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| Affiliation: | (State Key Laboratory ofRobotics,Shenyang Institute ofAutomation,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110016,China;University of Chinese Academy of Sciences,Beijing 100049,China) |
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| Abstract: | For the online monitoring of underwater vehicle thruster system, a thruster system fault diagnosis method with online learning ability is proposed. Firstly, time delay of the thruster system is estimated online by analyzing the changing trend of correlationship. After that, online modeling of the relationship between control voltage and rotating speed is implemented use the data acquired during operation. In order to improve the modeling accuracy, the particle swarm optimization algorithm is utilized to optimize the model order and modeling data volume. To adapt to the changes of environment and system status during operation, an online update mechanism of the model is designed. Based on the online update mechanism, an adaptive fault detection method that does not rely on traditional thresholds is proposed. Finally, the effectiveness of the proposed algorithm is verified by sea trial data and pool tests. |
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| Keywords: | underwater vehicle thruster system adaptive fault detection time delay estimation |
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