一种基于SUKF的广义行为环境建模及在远程AUV推进系统的应用研究

针对自主水下机器人对广义行为环境自适应能力差的问题,给出基于平方根无色卡尔曼滤波的广义行为环境建模方法。在广义行为环境的离线参考模型中,有一些参数是时变的、是无法事先预知的,必须通过传感器探测的信息进行实时估计和预测。采用平方根无色卡尔曼滤波算法,根据在线传感器信息以及离线参考模型,实时地估计出广义行为环境的状态和参数。主要研究自主水下机器人自身行为环境建模,以远程水下自主机器人的推进系统为例,构建一种推进器效率损失因子的故障模型结构,应用平方根无色卡尔曼滤波对水下自主机器人的状态和推进器故障参数进行在线联合估计。利用远程自主水下机器人的数学模型进行仿真验证,试验结果表明了算法的有效性,并对影响平方根无色卡尔曼滤波算法估计性能的因素进行了分析。     

基于UKF的水下机器人执行器故障检测方法研究

水下机器人需要在无人干预或者少量干预的情况下自主地完成使命,这就要求它在作业过程中能够自主地检测子系统、传感器和执行器的故障.在确认发生故障的时候,它还需要尽最大可能地进行修复,以确保使命的顺利进行.水下机器人常用的执行器包括推进器、舵、机械手等,针对推进器的故障检测问题,首先建立了水下机器人的运动学和动力学模型,然后采用UKF对系统的状态和参数进行联合估计,最后通过外场试验验证了算法的有效性和正确性.     

一种水下机器人的水面平台控制系统设计

针对水下机器人进行水下检测时的实时控制和监控问题,对水下机器人的推进器结构、运动方式、通讯方式、机体状态显示方式、运动控制方法等方面进行了研究,对水下机器人实现高效水下检测所需的各功能需求进行了归纳,提出了一种基于Lab VIEW的ROV水下机器人的水面平台控制系统,利用实验水池对水下机器人的人机交互界面和摄像头拍摄图像进行了测试。研究结果表明,该系统能实时监测水下机器人的各项性能数据,能够实现通讯连接、机体状态显示、运动控制等多项功能,实时控制响应迅速,实时监控效果好。     

基于可旋转推进器的水下机器人运动控制研究

通过对水下机器人在运动过程中的受力以及力矩分析,水下机器人采用可旋转推进器,所以要实现六自由度的运动只需要4个推进器,采用推进器布局。建立了基于可旋转推进器的水下机器人六自由度运动方程,并有针对性的对六自由度运动方程进行了简化。水下机器人运动控制的分析离不开动力学模型,所以对其进行受力和力矩分析是必要的。在简化的运动方程的基础上设计了运动控制系统,并使用Matlab对其进行仿真验证。     

警用水下机器人本体的设计

介绍了一种在水下进行刑事证物的探测识别、涉案水下现场搜索调查和水下证物打捞作业的警用水下机器人本体。机器人用两个主推进器和两个辅推进器实现其前后、浮潜和左右转弯运动,带有与控制相关的传感器和计算机以及水下探测设备,可实现水下机器人的自主定深航行,并具有对水下目标的光学探测和声纳探测能力。试验验证,该机器人能高效地完成作业使命任务,对水下机器人的设计有一定的借鉴参考价值。     

基于STF-Singer模型的AUV传感器故障诊断

为了实现水下机器人传感器系统的故障诊断和信号恢复,将基于信号处理的方法和强跟踪滤波器方法相结合,提出了一种基于一阶时间相关函数的辛格模型和强跟踪滤波器理论的STF-Singer模型的故障诊断方法,该方法不依赖AUV精确的数学模型,对于AUV传感器故障有着很好的辨识能力。某型水下机器人的计算机仿真和海上试验的数据结果,验证了该方法的有效性和可行性。     

基于结构输出响应的传感器故障检测研究

针对土木工程结构健康监测系统的传感器故障问题,提出了基于随机子空间识别技术和Kalman估计器的传感器故障检测方法,实现了结构健康监测系统中故障传感器的检测。首先,通过正常传感器的量测数据,基于随机子空间识别方法重构了结构的离散状态空间模型;然后通过已识别出的结构离散状态空间模型构造多路Kalman估计器,产生输出残差;最后,通过文中提出的基于输出残差的传感器故障检测策略,实现了传感器故障的检测。文章通过某典型框架结构和ASCE Benchmark 4层钢框架缩尺模型,验证了文中所提故障检测方法的有效性和实用性。     

基于K-L散度的机械或传感器故障判别方法

机械故障诊断系统中，对同一监测部位通常采用双传感器配置（如水平和垂直方位）。文中首先运用核密度估计方法得到两传感器输出信号的概率密度函数估计，然后计算两输出信号间K-L（Kullbaek—Leiber）散度，并提出一种基于K-L散度值的机械或传感器故障判别准则。通过对一个齿轮减速箱实测振动信号和模拟的传感器故障信号的计算，可以发现，与无故障状态时K-L散度相比，监测部位出现机械故障时两传感器输出信号间K-L散度显著减小；而两传感器之一出现故障时其K-L散度显著增大。因此，两信号间K-L散度的变化可用于区别机械和传感器故障。     

传感器故障诊断实时监测系统设计的探讨

对大型设备运行状态监测和故障的早期诊断与预报活动中,传感器自身故障的及时发现与判别尤为重要。本文提出以设备子系统的（或某一功能的）传感器监测参数为划分原则,将单传感器的监测信号综合作为多传感器监测而进行信息的融合处理,从而判别子系统运行状态和传感器信号的真实性的构思,并探讨传感器故障诊断实时监测系统的设计方案。     

基于传感器的操作臂惯性参数在线识别神经网络模型

机器人惯性参数识别是机器人精确建模以及机器人控制和仿真的关键问题之一。机器人腕力传感器的接入将影响机器人系统的动力学特性 ,同时腕力传感器的输出也真实地反映了机器人的力作用和机器人末端的动力学特性。本文基于腕力传感器的输出信号 ,对在线识别机器人操作臂末端的惯性参数的方法进行了分析和研究 ,并建立了惯性参数在线识别的神经网络模型 ,网络学习后其权值即为辨识的惯性参数。     

基于盲源分离的旋转机械干扰消除技术研究

实际工厂环境中，用于状态监测与故障诊断的信号检测传感器，其所采集的机器信号(振动或声音)中，不可避免地混杂有来自于相邻设备以及周围环境的干扰，这对于机器健康状态的准确监测是很不利的。这里研究利用盲源分离技术分离(去除)这些无用的外来干扰，以提高故障诊断的准确性。盲源分离是一个很独特的盲信号分析与处理工具，在机械设备监测与诊断领域有着很好的应用前景。仿真实验以及现实世界的声源信号分离实验结果，证实了盲源分离方法在机械设备干扰去除方面的有效性。     

Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study

This paper presents a case study of the application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine. Using measurement signals taken from the sensors/actuators which are present in a modern automotive vehicle, a data-driven diagnostic model is built for condition monitoring purposes. Detailed investigations have shown that measured signals taken from the experimental test-bed often contain redundant information and noise due to the nature of the process. In order to deliver a clear interpretation of these measured signals, they therefore need to undergo a ‘compression’ and an ‘extraction’ stage in the modelling process. It is at this stage that the proposed data-driven monitoring technique plays a significant role by taking only the important information of the original measured signals for fault diagnosis purposes. The status of the engine's performance is then monitored using this diagnostic model. This condition monitoring process involves two separate stages of fault detection and root-cause diagnosis.The effectiveness of this diagnostic model was validated using an experimental automotive 1.9 L four-cylinder diesel engine embedded in a chassis dynamometer in an engine test-bed. Two joint diagnostics plots were used to provide an accurate and sensitive fault detection process. Using the proposed model, small air leaks in the inlet manifold plenum chamber with a diameter size of 2–6 mm were accurately detected. Further analyses using contribution to T² and Q statistics show the effect of these air leaks on fuel consumption. It was later discovered that these air leaks may contribute to emissions fault.In comparison to the existing model-based approaches, the proposed method has several benefits: (i) it makes no simplifying assumptions, as the model is built entirely from the measured signals; (ii) it is simple and straight-forward; (iii) there is no additional hardware required for modelling; (iv) it is a time and cost-efficient way to deliver condition monitoring (i.e. fault diagnosis application); (v) it is capable of pin-pointing the root-cause and the effect of the problem; and (vi) it is feasible to be implemented in practice.     

基于LabVIEW的油田注水泵机组在线远程监测系统

设计了基于LabVIEW的油田注水泵机组在线远程监测系统,利用加速度传感器和热电偶传感器采集机组振动和温度参数,利用网络技术和数据库技术,利用编制的软件对信号进行远程传输、实时监测和故障诊断,实现对多级离心式注水泵机组在线远程监测和故障诊断。极大地提高了注水泵机组的测试与故障诊断的时效性。     

Design and analysis of a new AUV’s sliding control system based on dynamic boundary layer

The new AUV driven by multi-vectored thrusters not only has unique kinematic characteristics during the actual cruise but also exists uncertain factors such as hydrodynamic coefficients perturbation and unknown interference of tail fluid,which bring difficult to the stability of the AUV’s control system.In order to solve the nonlinear term and unmodeled dynamics existing in the new AUV’s attitude control and the disturbances caused by the external marine environment,a second-order sliding mode controller with double-loop structure that considering the dynamic characteristics of the rudder actuators is designed,which improves the robustness of the system and avoids the control failure caused by the problem that the design theory of the sliding mode controller does not match with the actual application conditions.In order to avoid the loss of the sliding mode caused by the amplitude and rate constraints of the rudder actuator in the new AUV’s attitude control,the dynamic boundary layer method is used to adjust the sliding boundary layer thickness so as to obtain the best anti-chattering effects.Then the impacts of system parameters,rudder actuator’s constraints and boundary layer on the sliding mode controller are computed and analyzed to verify the effectiveness and robustness of the sliding mode controller based on dynamic boundary layer.The computational results show that the original divergent second-order sliding mode controller can still effectively implement the AUV’s attitude control through dynamically adjusting the sliding boundary layer thickness.The dynamic boundary layer method ensures the stability of the system and does not exceed the amplitude constraint of the rudder actuator,which provides a theoretical guidance and technical support for the control system design of the new AUV in real complex sea conditions.     

Impact of plasma noise on a direct thrust measurement system

In order to evaluate the accuracy and sensitivity of a pendulum-type thrust measurement system, a linear variable differential transformer (LVDT) and a laser optical displacement sensor have been used simultaneously to determine the displacement resulting from an applied thrust. The LVDT sensor uses an analog interface, whereas the laser sensor uses a digital interface to communicate the displacement readings to the data acquisition equipment. The data collected by both sensors show good agreement for static mass calibrations and validation with a cold gas thruster. However, the data obtained using the LVDT deviate significantly from that of the laser sensor when operating two varieties of plasma thrusters: a radio frequency (RF) driven plasma thruster, and a DC powered plasma thruster. Results establish that even with appropriate shielding and signal filtering the LVDT sensor is subject to plasma noise and radio frequency interactions which result in anomalous thrust readings. Experimental data show that the thrust determined using the LVDT system in a direct current plasma environment and a RF discharge is approximately a factor of three higher than the thrust values obtained using a laser sensor system for the operating conditions investigated. These findings are of significance to the electric propulsion community as LVDT sensors are often utilized in thrust measurement systems and accurate thrust measurement and the reproducibility of thrust data is key to analyzing thruster performance. Methods are proposed to evaluate system susceptibility to plasma noise and an effective filtering scheme presented for DC discharges.     

Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis

With a view to detecting incipient failures in large-size low-speed rolling bearings and ensuring minimal effect of subjectivity on the process, a new data-driven multivariate and multiscale statistical monitoring method is proposed. The proposed method which combines the Principal Component Analysis (PCA) multivariate monitoring approach and the Ensemble Empirical Mode Decomposition (EEMD) method, which adaptively decomposes signals into various time scales, was called the EEMD-based multiscale PCA (EEMD–MSPCA). The method is very general in nature, which is why it could also be used in different areas and for various tasks. It can be used for controlling each time scale of decomposition or only the selected ones, for multivariate and multiscale filtering or for monitoring system operation on the basis of reconstructed i.e. filtered signals. The efficiency of the proposed EEMD–MSPCA method for the task of bearing condition monitoring and signal filtering was evaluated on simulated as well as on actual vibration and Acoustic Emission (AE) signals measured on a purpose built test stand. The fact that the proposed method is able to identify the local bearing defect of a very small size indicates that AE and vibration signals carry sufficient information on the bearing condition and that the proposed EEMD–MSPCA method ensures high-reliability bearing fault detection.     

传感器数据证实技术应用研究

传感器技术是现代测试和控制技术的灵魂 ,传感器输出信号的质量关系到整个系统性能的好坏程度。传感器数据证实技术是测控领域中的关键技术之一 ,文中提出了基于神经网络预测器的传感器数据证实技术。首先利用神经网络对传感器输出的时间序列建立预测模型 ,然后利用该模型预测出传感器输出值 ,并用该值与传感器实际输出值之差判断传感器实际输出的可靠性和有效性 ,进而减小不同环境噪声对传感器输出数据的影响。同时 ,为了实现在线应用 ,采用了遗传算法对神经网络的优化技术。实践表明 :文中提出的基于神经网络的传感器数据证实技术对缓变信号和快变信号都有很好的效果     

低功率水工质脉冲等离子体推进器的工作特性

为了解决Teflon脉冲等离子体推进器存在的性能低、有污染等问题,设计了以水为工质的脉冲等离子体推进器系统,并研究了它的主要工作指标的能量阈值.分析了水工质脉冲等离子体推进器系统的放电类型形成条件及原因,通过放电电压和电流测试实验,对工作能量阈值进行了实验研究,得到了稳定运行能量对应的储能电容值.实验结果表明:在足够高...