无人机远程故障测试与诊断技术研究

为了确保众多无人机能够远程共享测试设备与技术人员等资源,设计了一种基于DataSocket技术和信息融合理论的远程故障测试与诊断系统;该系统采用PXI总线模块作为数据采集设备,利用DataSocket技术和虚拟仪器实现测试数据的远程传输;采用基于D-S证据理论的多智能诊断算法数据融合技术,提高故障诊断的准确率;同时为了便于故障信息的管理,把诊断出的结果存放在数据库中,远端使用B/S架构获取诊断数据;该远程故障测试与诊断系统可提高故障诊断正确率,实现故障信息的统一管理,节约成本提高效率,有一定的工程应用价值。     

面向MES的生产线数据采集系统的研究

为满足生产管理与在线监测的需要,设计并实现了一个面向MES的生产线数据采集系统;该系统采用了一种基于PROFIBUS-DP和OPC技术的数据采集方法,实现了现场采集数据的高速及稳定的传输,保证了不同设备之间良好的兼容性;用户可以通过OPC客户端完成对生产线状态的远程监控,当采集到的参数超过设定的阀值时,系统可以自动报警并做出相对应的处理;经过试验表明,该系统各项性能良好,已经实现MES底层硬件和软件的深度融合,在生产线监控方面有着很重要的应用价值。     

便携式数据采集控制系统的设计与实现

在狭小空间或是恶劣环境下进行数据采集,传统的数据采集设备占用空间大、传输速率慢,不方便携带。通过分析这些缺点,设计了一种基于WiFi无线网络技术的便携式的无线数据采集控制系统。整个系统包括数据采集单元、数据传输单元和数据控制单元三大部分。该数据采集控制系统能够在WiFi网络覆盖区域内,以手持Android小型终端为控制终端,以无线网络为传输通道,实时控制数据采集卡,并读取采集数据,即时数据可帮助用户更加可靠的分析实际情况,具有实际应用价值。     

基于MPC8270的处理器板卡设计

随着海洋地震勘探发展,为了实现海洋地震勘探采集数据的实时传输和处理,设计了一种基于MPC8270的地震数据采集处理板卡;根据海洋地震数据采集处理的实时性和大数据量要求以及MPC8270处理器的特性,设计了基于MPC8270和FPGA的CPCI总线通信结构,能够实现地震数据的高速实时传输;详细介绍了MPC8270的关键外围电路、FPGA及CPCI总线电路的硬件设计及实现方法,并在实际应用中给出了板卡嵌入式操作系统VxWorks配置的实现方式;板卡以MPC8270作为核心处理器,以FPGA为控制核心电路,以VxWorks为实时操作系统,实现地震数据的高速处理和CPCI总线实时传输功能;经过多次实验室内部测试及海上实际生产应用,结果表明地震数据采集处理板卡能够实现地震数据的高速处理和实时传输,并且板卡性能稳定可靠;板卡的设计简单,结构通用,对数据处理、总线控制和信息交换等领域相关系统的开发具有一定的参考价值。     

一种多通道高速数据采集系统软件北大核心CSCD

多通道高速数据采集软件面临:被测信号多样化,通道设置及管理难度大;传输数据并发量大,传输稳定性和瞬时传输速度要求高;测量数据量大,后处理及速度快;实验数据重要性高,测试软件必须稳定可靠的挑战。软件采用模块化+图形化设计,使用SCPI命令对设备及通道进行设置;测试了75台示波器同时上传数据的传输速度;对比了LabVIEW波形显示和GDI+波形绘制的区别;完成了上位机故障情况下数据获取功能设计。结果表明:该数据采集软件可完成75台示波器的管理,实现300通道数据的上传、处理、显示、存储。100 kpts存储深度300通道数据上传时间为9.7 s,300通道波形刷新时间为0.91 s。     

基于单片机的霍尔效应实验数据采集和显示系统的研发

介绍了一种基于单片机的数据采集、传输和显示系统,该系统是以大学物理实验中的霍尔效应测磁场实验为基础,阐述了其结构、功能、硬件及软件的设计过程。本系统采用单片机通过R232协议和PC机进行通信,通过A/D转换模块、STC89C52RC单片机主控模块、串口通信、数据处理和显示等模块来实现一种高效的数据采集、传输和显示,该系统在实验教学中具有一定的应用前景和商业价值。     

基于3G的能耗数据采集器设计

针对大型公共建筑实时监控能耗的需求,借助3G网络传输的优势,提出了一种基于3G的能耗数据采集器的设计方案。该方案使用STM32为主控制器,根据Modbus通信规约通过RS485方式实现对智能电表的能耗数据采集,再将数据通过3G无线网络远传至服务器。重点设计了采集器的硬件电路,数据采集与传输驱动程序。经实验室测试,该采集器能实时、准确地采集数据并传送至服务器,性能稳定可靠,能够满足能耗监测系统的需求。     

基于列车智能显示器故障记录处理的设计与研究

列车在外部环境的影响下导致各设备的软硬件产生缺陷从而引发故障,在一定程度上影响了列车的性能和安全。显示器是列车必不可少的重要部件,是人机交互的桥梁,也是列车各设备故障状态信息显示的窗口。针对传统列车显示器的故障误报和易产生故障空白记录的现象,文章设计和实现了基于故障码的列车显示器故障信息搜索和分析方法,大大提高了列车显示器应用设计人员的开发效率,减少了故障记录过程的工作量。并基于实际测试环境对该方法进行了实验,结果表明,该方法对列车显示器故障记录的处理具有高效性、健壮性,并保证了显示器故障信息显示的准确性。     

静态磁场测试平台的采集监控系统的设计与实现

介绍了静态磁场测试平台的结构和特点,详细描述了采集监控系统的体系架构,该系统是由数据采集管理系统和操作监控平台组成的。根据该系统的特点,设计了基于Qt的数据采集管理系统和基于EPICS的操作监控平台。该系统实现了对静态磁场测试平台的实时控制、设备状态检测及监控、数据采集、存储及查看。该应用运行稳定,能满足静态磁场测试平台对采集监控的需求。     

基于ZigBee的大型公共建筑能耗采集器设计

针对大型公共建筑能耗实时采集系统中存在的布线复杂、通信成本高、安全性低等问题,提出一种基于ZigBee技术的能耗数据采集器设计方案。该方案采用STM32做主控制器, ZigBee采集单元通过RS485接口实现与基于Modbus协议的智能电表之间的数据通信,并采用ZigBee无线网络将数据发送至协调器,协调器通过USART方式与主控制器进行通信;主控制器利用以太网将数据发送至上位机。通过软硬件设计,以XY194E智能电表为例,对其电能参数进行测试采集,结果表明该方案克服了传统人工采集传输的不足,提高了数据采集传输的实时性、准确性,能够满足能耗采集系统的要求。     

SDN框架下不良数据计算机网络管理系统设计

近年来,随着互联网的飞速发展,网络信息交互量不断增大。各类不良网络交互信息数据充斥着网络交互空间,给网络社交秩序造成破坏。为此,一系列网络不良数据管理系统孕育而出。通过长期实践发现,传统不良数据计算机网络管理系统的管理方式存在不良数据动态检测性不强、不良信息数据内容识别度低、不良数据管理逻辑性差等问题。通过与SDN框架的优势相结合,提出SDN框架下不良数据计算机网络管理系统设计。采用NVR数据量交互模块、数据特征DNA内容识别算法与大数据统筹规划模块,对传统不良数据管理系统方式中存在的问题进行解决。通过实验证明,提出的SDN框架下不良数据计算机网络管理系统比传统的数据管理系统,在不良数据识别、检测、统计、管理等方面上具有绝对优势。     

经验模态分解与样本熵在并网型光伏逆变器故障诊断中的应用

摘要：针对光伏并网逆变器电路中故障信号的非线性、非平稳特点,提出一种基于经验模态分解(EMD)和样本熵(SampEn)的故障诊断方法。首先,利用经验模态分解对逆变器的三相输出电压进行分解,得到有限个本征模式分量(IMF),从中选取包含故障主要信息的前几个本征模式分量提取故障信息。然后,计算本征模式分量的样本熵,从而得到用于故障诊断的特征向量;最后,将逆变器开路故障进行分类和编码,将故障特征向量输入BP神经网络进行模式识别,从而达到故障诊断的目的。在Matlab环境下对光伏并网逆变器的故障诊断进行了实验,实验结果证明了文中方法能实现对光伏并网逆变器的故障诊断,且与小波包变换相比,该方法具有诊断效率高和准确度高等特点。     

Development of capacitance-based and impedance-based wireless sensors and sensor nodes for structural health monitoring applications

A field demonstration of a new and hybrid wireless sensing network paradigm for structural health monitoring (SHM) is presented. In this paradigm, both power and data interrogation commands are conveyed via a mobile agent that is sent to each sensor node to perform individual interrogations, which can alleviate several limitations of traditional sensing networks. This paper will discuss such prototype systems, which will be used to interrogate capacitive-based and impedance-based sensors for SHM applications. The capacitive-based wireless sensor node is specifically built to collect peak displacement measurements. In addition, a wireless sensor node for collecting electromechanical impedance data has also been developed. Both sensor nodes are specifically designed to accept various power sources and to be wirelessly triggered on an as-needed basis so that they can be used for the hybrid sensing network approach. The capabilities of these miniaturized and portable devices are demonstrated in the laboratory and the field, which was performed at the Alamosa Canyon Bridge in southern New Mexico.     

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Bad meteorological conditions may reduce the reliability of power communication equipment, which can increase the distortion possibility of fault information in the communication process, hence raising its uncertainty and incompleteness. To address the issue, this paper proposes a fault diagnosis method for transmission networks considering meteorological factors. Firstly, a spiking neural P system considering a meteorological living environment and its matrix reasoning algorithm are designed. Secondly, based on the topology structure of the target power transmission network and the action logic of its protection devices, a diagnosis model based on the spiking neural P system considering the meteorological living environment is built for each suspicious fault transmission line. Following this, the action messages of protection devices and corresponding temporal order information are used to obtain initial pulse values of input neurons of the diagnosis model, which are then modified with the gray fuzzy theory. Finally, the matrix reasoning algorithm of each model is executed in a parallel manner to obtain diagnosis results. Experiment results achieved out on IEEE 39-bus system show the feasibility and effectiveness of the proposed method.     

基于LABVIEW的旋转机构测控系统设计

针对旋转测试的任务要求,设计了一套基于LabView开发环境下的测试系统和一套基于PLC控制技术的控制系统,并利用OPC通讯方式,实现了LabView与PLC之间的数据交互。利用LabView开发了多种软件功能,丰富了测控系统的数据处理和显示功能。经试验结果表明：该测控系统已达到预期设计效果。     

A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.     

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.     

基于互联网+的高校能源管理信息系统设计与开发

为了实现高校能源管理的信息共享,提高对高校能源信息管理的智能水平,提出一种基于互联网+的高校能源管理信息系统开发设计方案。系统采用感知层、网络层和应用层的三层结构,采用RFID、 条形码、蓝牙、红外等数据信息感知技术进行高校能源信息的原始采集,在网络层通过ZigBee 和无线通信技术进行信息融合传输,在控制设备中导入原始数据,在互联网+环境下建立数据处理中心,根据能源管理系统的现实需求进行信息融合和数据存储管理,设计嵌入式控制器对串口、并口、USB端口、以太网口及GPIB接口进行集成控制,在Linux内核下实现高校能源管理信息系统应用程序开发。系统测试结果表明,采用该系统进行高校能源管理,具有较好的信息存储、信息调度和信息检索能力。     

人工神经网络结合多光谱遥感数据用于立地质量评价

多光谱遥感数据蕴含着大量的地表立地信息,而传统立地质量评价体系主要使用了人工地面调查数据。为了建立一套有效的立地质量评价体系,以内蒙古赤峰市旺业甸林场为研究对象,基于研究区域的多光谱遥感数据结合地面小班调查数据,采用一种改进的反向传播人工神经网络(back Propagation artificial neural network,BPANN)模型,以落叶松为例,建立了遥感光谱因子结合立地因子与地位指数关系的神经网络模型,对研究区域的小班进行立地质量评价研究。通过训练数据集的敏感度分析剔除弱相关或不相关的因子,简化了神经网络的规模,提高了网络的训练效率,得到了最优的地位指数预测模型,模型的预测精度达到95.36%,与使用传统小班调查数据建立的神经网络模型的预测结果进行了比较,精度提高了9.83%,说明使用多光谱遥感数据+小班调查数据确定的落叶松地位指数预测模型具有最高的预测精度。多光谱遥感数据十分适用于森林立地质量评价,改进BP神经网络具有理想的预测精度,充分证实了该方法的有效性和优越性。     

基于LabWindows/CVI的楼宇供水防泄漏自动测控系统的设计

针对智能楼宇供水管网的泄漏测控问题,提出了一种基于LabWindows/CVI虚拟仪器的新型测控系统设计方案。系统在结构上分为测控节点、楼层节点和中心节点三部分,基于ZigBee无线传感网络实现硬件互联。测控节点采用光电传感电路检测泄漏,发生泄漏时自动切断对应支路水源,并将报警信息经楼层节点发往管理中心,中心节点基于LabWindows/CVI虚拟仪器程序接收并处理数据,实现泄漏定位、示警及相关数据库操作。测试表明,系统的平均泄漏检出率达99.8%、判定准确率达99.9%,测控精度高、可靠性强,能够很好地满足智能楼宇供水泄漏的高精度自动测控需求、保障建筑楼体及内部财物免受漏水威胁。