多功能潜水器系统故障诊断技术研究
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摘要
水下机器人在海洋开发和军事应用方面有着极为广阔的应用前景,已经成为完成多种水下任务的重要工具。水下机器人工作的海洋环境极其恶劣以及其需要完成的任务复杂多变,因此设计一个可靠性高的控制系统来保证它安全可靠的工作非常必要。实现水下机器人控制系统故障自主诊断是其智能水平的核心体现也是研究人员迫切需要解决的课题。
本文的主要工作就是完成多功能水下机器人系统的自主故障诊断,针对传感器系统及推力器系统开展诊断研究以提高控制系统的总体性能,进而提高水下机器人在未知海洋环境下的生命力和其它特性。
论文首先对水下机器人传感器信息做小波变换,利用小波变换极值点来检测信号的突变故障。为了消除环境噪声的影响,引入了阈值法,通过对小波变换的高频系数设置阈值来消除噪声干扰。试验结果表明该方法能提高对突变信号检测的精度;针对定位声纳输出数据的振荡情况,采用了线性平滑方法。设计了三次曲线拟合及kalman滤波并与线性平滑进行了对比试验,试验结果表明:对水下机器人这样一个特定的对象,线性平滑方法不仅处理简单而且直接有效。
针对推力器故障诊断,设计了一个非线性滑模观测器,构造了一种新的切换函数来代替滑模观测器中的符号函数,以克服符号函数在零点附近切换时的突变,从而达到了消除滑模观测器抖振的目的;基于阈值的故障诊断方法存在故障判断不连续及误报率高的弱点,为了提高故障诊断精度,探讨了模糊诊断方法,设计了一个模糊残差评估器来分析残差信号并用于推进器故障诊断。仿真结果表明模糊故障诊断方法能够提高故障诊断的精度,使得故障诊断的鲁棒性增强。
本文还采用了一种改进的小波神经网络结构,推导了其学习算法,利用训练好的小波网络完成了对水下机器人的系统辨识,通过对比小波网络的输出与实际传感器的输出进行故障诊断。完成了基于小波网络的推力器故障诊断,得到了令人满意的结果。这种方法减少了神经网络的输入量,使得神经网络的结构相对简单。
研究结果表明,本文应用的故障诊断方法可以实现对水下机器人控制系统的故障诊断,在水下机器人技术中有着重要的现实意义。
With the development of the activities in deep sea, the application of underwater vehicles is widespread. Underwater vehicle that works in very tough environment and needs to finish many tasks, therefore, it is very crucial to design a reliable control system to guarantee its safety. To realize the self fault diagnosis of the underwater vehicle's control system is the core symbol of the intelligent level and is the problem that the researchers need to deal with.
Research on system fault diagnosis for multifunction underwater vehicle is undertaken in this thesis. The main task is studying the faults of the sensor system and thrusters in order to improve the performance of the control system, therefore, the underwater vehicle can enhance its survivability when it works in the unknown sea environment.
Firstly, the wavelet transform is undertaken for the sensor information of the underwater vehicle, and the extreme points of the wavelet transform are used to detect the jumping faults of the signal. In order to decrease the noise's influence, the threshold method is brought in. The disturbance of the noise can be bucked by setting the threshold for the high frequency parameters of the wavelet transform. The experiment results tells that this method can enhance the detect accuracy of the jumping signal. As to the oscillation of the outputs of the positioning sonar, the linear smoothing method is adopted. The cubical curve fitting and kalman filter are designed and comparison experiments are conducted among them, and the experiment results say: underwater vehicle as the specific research plant, linear smoothing method is not only very simple but also direct and effective.
To deal with the thruster fault diagnosis, a nonlinear sliding mode observer is designed. A new transfer function is constructed to replace the symbol function in sliding mode observer to overcome the jump which is caused by the symbol function transfers near the origin zone. Therefore, the aim to decrease the buffeting of the sliding mode observer can be realized. Because of the discontinuity and distort of the fault diagnosis method based on threshold, the fuzzy fault diagnosis is discussed to improve the accuracy of the fault diagnosis. A fuzzy residual evaluator is designed to analyze the residual signal and it is applied to thruster fault diagnosis. The simulation results indicate that fuzzy fault diagnosis method can heighten the accuracy of the fault diagnosis, and the robustness of the fault diagnosis can be strengthened.
An improved wavelet neural network structure is adopted here. The learning algorithm is deduced. The wavelet neural network which trained well is applied to do the system identification for the underwater vehicle, and the fault diagnosis can be achieved by comparing the outputs between the wavelet neural network and real sensors. The thruster fault diagnosis based on wavelet neural network is finished and satisfied results are obtained. This method can decrease the inputs for the neural network. Therefore, the neural network structure can be simplified.
The research results display that the fault diagnosis methods presented here can realize the fault diagnosis of the control system, and it is very useful in the field of underwater vehicle.
本文的主要工作就是完成多功能水下机器人系统的自主故障诊断,针对传感器系统及推力器系统开展诊断研究以提高控制系统的总体性能,进而提高水下机器人在未知海洋环境下的生命力和其它特性。
论文首先对水下机器人传感器信息做小波变换,利用小波变换极值点来检测信号的突变故障。为了消除环境噪声的影响,引入了阈值法,通过对小波变换的高频系数设置阈值来消除噪声干扰。试验结果表明该方法能提高对突变信号检测的精度;针对定位声纳输出数据的振荡情况,采用了线性平滑方法。设计了三次曲线拟合及kalman滤波并与线性平滑进行了对比试验,试验结果表明:对水下机器人这样一个特定的对象,线性平滑方法不仅处理简单而且直接有效。
针对推力器故障诊断,设计了一个非线性滑模观测器,构造了一种新的切换函数来代替滑模观测器中的符号函数,以克服符号函数在零点附近切换时的突变,从而达到了消除滑模观测器抖振的目的;基于阈值的故障诊断方法存在故障判断不连续及误报率高的弱点,为了提高故障诊断精度,探讨了模糊诊断方法,设计了一个模糊残差评估器来分析残差信号并用于推进器故障诊断。仿真结果表明模糊故障诊断方法能够提高故障诊断的精度,使得故障诊断的鲁棒性增强。
本文还采用了一种改进的小波神经网络结构,推导了其学习算法,利用训练好的小波网络完成了对水下机器人的系统辨识,通过对比小波网络的输出与实际传感器的输出进行故障诊断。完成了基于小波网络的推力器故障诊断,得到了令人满意的结果。这种方法减少了神经网络的输入量,使得神经网络的结构相对简单。
研究结果表明,本文应用的故障诊断方法可以实现对水下机器人控制系统的故障诊断,在水下机器人技术中有着重要的现实意义。
With the development of the activities in deep sea, the application of underwater vehicles is widespread. Underwater vehicle that works in very tough environment and needs to finish many tasks, therefore, it is very crucial to design a reliable control system to guarantee its safety. To realize the self fault diagnosis of the underwater vehicle's control system is the core symbol of the intelligent level and is the problem that the researchers need to deal with.
Research on system fault diagnosis for multifunction underwater vehicle is undertaken in this thesis. The main task is studying the faults of the sensor system and thrusters in order to improve the performance of the control system, therefore, the underwater vehicle can enhance its survivability when it works in the unknown sea environment.
Firstly, the wavelet transform is undertaken for the sensor information of the underwater vehicle, and the extreme points of the wavelet transform are used to detect the jumping faults of the signal. In order to decrease the noise's influence, the threshold method is brought in. The disturbance of the noise can be bucked by setting the threshold for the high frequency parameters of the wavelet transform. The experiment results tells that this method can enhance the detect accuracy of the jumping signal. As to the oscillation of the outputs of the positioning sonar, the linear smoothing method is adopted. The cubical curve fitting and kalman filter are designed and comparison experiments are conducted among them, and the experiment results say: underwater vehicle as the specific research plant, linear smoothing method is not only very simple but also direct and effective.
To deal with the thruster fault diagnosis, a nonlinear sliding mode observer is designed. A new transfer function is constructed to replace the symbol function in sliding mode observer to overcome the jump which is caused by the symbol function transfers near the origin zone. Therefore, the aim to decrease the buffeting of the sliding mode observer can be realized. Because of the discontinuity and distort of the fault diagnosis method based on threshold, the fuzzy fault diagnosis is discussed to improve the accuracy of the fault diagnosis. A fuzzy residual evaluator is designed to analyze the residual signal and it is applied to thruster fault diagnosis. The simulation results indicate that fuzzy fault diagnosis method can heighten the accuracy of the fault diagnosis, and the robustness of the fault diagnosis can be strengthened.
An improved wavelet neural network structure is adopted here. The learning algorithm is deduced. The wavelet neural network which trained well is applied to do the system identification for the underwater vehicle, and the fault diagnosis can be achieved by comparing the outputs between the wavelet neural network and real sensors. The thruster fault diagnosis based on wavelet neural network is finished and satisfied results are obtained. This method can decrease the inputs for the neural network. Therefore, the neural network structure can be simplified.
The research results display that the fault diagnosis methods presented here can realize the fault diagnosis of the control system, and it is very useful in the field of underwater vehicle.
引文
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[2]朱继懋.潜水器设计.上海交通大学出版社.1992.
[3]徐玉如,苏玉民,庞永杰.海洋空间智能无人运载器技术发展展望.中国舰船研究.2006,1(3).
[4]D.R.Yoerger,A.M.Bradley,B.B.Walden,A Deep Ocean AUV for Scientific Seafloor Survey,The Autonomous BENTHIC Explorer,Unmanned Systems,9(2),Spring 1991:17-23P.
[5]王丽荣,丁凯.基于小波网络的水下机器人执行器故障诊断.系统仿真学报.2007.
[6]Gan Yong,Sun Yushan,Wan Lei,Pang Yongjie.Motion Control System Architecture of Underwater Robot,Proceedings of the 6th World Congress on Control and Automation.Dalian,China.June,21-23,2006:8876-8880P
[7]周小勇.小波分析在故障诊断中的应用.上海海运学院硕士学位论文.2001:1-7页.
[8]Alessandri,A.,Caccia,M.,Veruggio,G..Fault detection of actuator faults in unmanned underwater vehicles.Control Engineering Practice,1999(7):357-368P.
[9]何勇,李增芳.智能化故障诊断技术的研究与应用.浙江大学学报(农业与生命科学版).2003,29(2):119-124页.
[10]王日新,徐敏强,宋政吉.航天器推进系统故障的面向时念检测和诊断.推进技术,2002,23(1):11-14页.
[11]Takai,M.,Ura,T..Development of a system to diagnose autonomous underwater vehicle.International Journal of Systems Science,1999,30(9),981-988P
[12]Edin Omerdic,Geoff Roberts.Thruster fault diagnosis and accommodation for open-frame underwater vehicles.Control Engineering Practice,2004(12):1575-1598P
[13]Kelvin Hamilton,Dave Lane,Nick Taylor,Keith Brown.Fault Diagnosis on Autonomous Rotootic Vehicles with RECOVERY;An Integrated Heterogeneous-Knowledge Approach.2001 IEEE Conference on Robotics and Automation.2001.5(21-25):3232-3238P
[14]Tarun Kanti Podder,Nilanjan Sarkar.Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy.Robotics and Autonomous Systems.2001,34(1):39o52P
[15]A.Alessandri.Fault diagnosis for nonlinear systems using a bank of neural estimators.Computers in Industry 2003(52):271-289P
[16]刘建成.水下机器人推进系统故障诊断与容错控制研究.哈尔滨工程大学博士学位论文,2003.
[17]王玉甲,张铭均.基于模糊神经网络的水下机器人实时状态检测模型.中国造船.2005,46(1):71-79页.
[18]王丽荣.水下机器人控制系统故障诊断研究.哈尔滨工程大学博士学位论文,2005.
[19]方少吉.水下机器人控制系统故障诊断与容错控制研究.哈尔滨工程大学硕士学位论文,2007.
[20]A.J.Healey,F.Bahrke,J.Navarrete.Failure Diagnostics for Underwater Vehicles:A Neural Network Approach.Manoeuvring and Control of Marine Craft.1990:294-305P
[21]周东华,叶银忠.现代故障诊断与容错控制.清华大学出版社.2000:1-10,46-57页.
[22]闻新,张洪钺,周露.控制系统的故障诊断和容错控制.机械工业出版社.1998:1-5,70-144页.
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