测控装备协同故障诊断系统研究

掌握地面测控系统的技术状态、尽快排除装备故障,是保证发射活动顺利实施的关键;根据测控装备的使用特点提出了协同故障诊断系统保障需求,针对装备的组成特点及结构原理,采用虚拟仪器技术、DataSocket技术、LabVIEW以及PXI总线系统架构,提出了基于故障树诊断和基于案例的故障诊断的专家系统,设计了基于故障树和故障字典双模式统一存储结构的信息库;最后,通过对装备外测频综机柜的故障诊断实例,对系统协同条件下故障树诊断进行了验证;结果表明系统满足功能设计要求,诊断结果可靠,人机交互友善,操作方便。     

基于模糊控制的无人水面艇直线路径跟踪方法

针对直流电机驱动固定双桨的无人水面艇,提出一种基于模糊控制的直线路径跟踪方法;利用无人水面艇到目标路径的垂直距离以及无人水面艇的实际航向与给定路径方向的差值来确定无人水面艇的当前状态,根据模糊推理的方法实时调整左右两侧推进电机的输入电压,进而改变无人水面艇的运动状态,实现无人水面艇自主直线路径跟踪;模糊控制器采用双输入双输出的控制结构,无需建立精确的控制器模型,在无人水面艇初始位置相对于跟踪直线的不同位置关系情况下进行了仿真实验,并与PID控制器的性能进行了比较,仿真结果表明:在初始航向偏差角较大时,如2π/3、π时,该方法克服了采用固定参数PID控制方法时出现的大迴转现象;在初始航向偏差角较小时,如π/4、3π/7、π/2时,该方法在超调量以及调节时间方面的直线跟踪性能优于固定参数PID控制方法。     

基于CLIPS的激光驱动器故障诊断专家系统设计

针对大型激光驱动器故障定位难,诊断、处置效率低的基本现状,提出利用专家系统进行故障诊断的基本思路,并以自动准直子系统为例,介绍了专家系统的体系架构、故障树模型、知识库设计和基于CLIPS工具的实现方法。初步应用结果表明,该专家系统有助于提高装置的故障诊断效率和智能化水平,增强系统可用性。     

基于改进证据推理规则的变压器故障诊断

为克服已有证据推理规则处理冲突证据时收敛慢甚至不收敛的缺陷,提出了一种按相关性对证据进行修正及对冲突信息合理分配的改进证据推理规则,并将其应用于变压器故障诊断。数值算例表明,改进证据推理规则可以充分利用变压器故障诊断中的一致性或冲突性多源故障征兆信息,提高变压器故障诊断的收敛速度,降低故障误判率。     

基于云-模糊融合技术的电机故障诊断

在传统模糊融合诊断系统基础上加入云模型,运用该模型的正、逆向云发生器、云运算原理,构建了云—模糊融合诊断系统;该系统结合了云模型的模糊性、随机性、统计性,在对电机故障进行决策,不仅有效地解决了传统诊断系统存在单一判别准则无法判别的局限,还拓宽了电机故障模糊判别准则,可有效地评定决策故障结果,使诊断结果更具有客观性与模糊性,最后通过仿真实例表明了新系统具有传统诊断系统所不具备的优势。     

基于GJB5000A的军用软件维护流程研究

为了规范我国军用软件的管理,以及使军方更方便对军用软件的维护,通过将GJB5000A、GJB1267等标准与实际工作情况进行结合,提出了一套军用软件维护流程;该流程设置了一个详细的软件维护指南,使军方人员清楚地了解如何维护军用软件,对于不断提高装备研制单位的软件产品质量起到了积极的作用。     

故障诊断专家系统的可拓知识表示和匹配研究

知识表示和匹配是设计专家系统的核心问题;首先通过引入基元理论,实现了产生式、语义网络、框架和案例的基元表示,建立了故障诊断专家系统的可拓知识表示模型,该模型包括激励基元、测量基元和结论基元3个部分;然后提出了先匹配激励基元再匹配测量基元的可拓知识匹配步骤,并构建了属性精确值和区间值混合的匹配度计算公式;最后以某型大气数据计算机的测试数据为例,与建立的可拓知识进行匹配,诊断结果与实际情况相符。     

基于故障矩阵的贝叶斯故障定位方法

故障的准确诊断和定位是云计算系统提供持续服务的前提条件。为了提高系统故障诊断和定位的性能,本文提出了一种基于故障矩阵的贝叶斯故障定位方法。首先,对云计算系统的软件结构进行了抽象,对事物进行了定义,并描述了事务的执行路径。其次,将系统运行的多个执行路径表示为故障矩阵,并给出了组件健康状态的逻辑命题表达式。最后,应用贝叶斯概率分析了系统故障的概率。实验表明,本文提出的方法与其它相关方法相比,故障识别的准确性更高,所用的执行时间更短。     

MQX-RTOS底层设备驱动构件可复用性研究

针对MQX实时操作系统下如何有效复用已经成熟的底层设备驱动构件,提高操作系统下底层设备驱动软件的开发速度、效率和质量等问题;在详细分析和研究MQX实时操作系统IO设备驱动三层管理体系构架基础上,将底层设备驱动和操作系统剥离,提出了MQX四层设备驱动管理体系框架;通过设计底层设备驱动接口构件,成功地复用了已有的底层设备驱动构件,避免底层驱动软件重复开发,有效缩短嵌入式软件开发的周期,并通过实例证明了该体系框架的可行性。      

基于扩展卡尔曼滤波的卫星组合姿态确定物理仿真技术研究

针对未来卫星应用的高精度、高可靠性定姿技术需求,设计了卫星单轴组合姿态确定物理仿真系统;采用太阳敏感器和光纤陀螺组合定姿模式,提出了一种扩展卡尔曼滤波组合姿态确定算法,用C语言进行了实验软件设计,系统采用DSP为处理器,还设计了多敏感器数据采集扩展接口;物理仿真实验结果:姿态确定精度优于0.5°;陀螺常值漂移估计的平均值为3.625758e-4°/s,证明了组合姿态确定算法的有效性和该项技术对工程应用的可行性。     

基于BP算法和ACO算法的故障诊断推理研究

BP算法在故障诊断领域已取得广泛应用,但其存在收敛速度慢且容易陷入局部最小值的缺陷,限制了其进一步的发展;ACO(Ant colony optimization)算法是一种模拟进化算法,已很好地应用于解决旅行商和资源两次分配等经典的优化问题,具有启发式收敛、正反馈以及分布式计算等优点;为此,将ACO算法引入BP算法故障诊断方法中,使用ACO算法对BP网络中的参数即权值、阈值以及学习率等进行优化,定义了一种结合ACO算法和BP算法能对故障进行诊断的新算法,并将其应用于具体的故障诊断实例中,最后,通过100组样本中的95组进行训练,并对剩余5组进行故障诊断,实验证明结合ACO算法和BP算法的新算法较传统的仅使用BP算法的诊断方法具有收敛速度快、诊断精确高以及训练性能好的优点。     

A Bearing Fault Diagnosis Method Based on PAVME and MEDE

When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.     

基于BP神经网络的卫星故障诊断方法

为了有效地利用卫星下传的海量遥测数据,在测试过程中对卫星进行实时的故障诊断,提出了一种基于BP神经网络的卫星故障诊断方法;该方法包括离线自主学习和实时在线故障诊断两部分;离线自主学习部分基于历史数据库和更新样本进行自主学习,学习获得神经网络模型存储于知识库;实时在线故障诊断部分依据相应的神经网络模型,对遥测数据进行实时在线的诊断;为了验证基于BP神经网络的卫星故障诊断方法的有效性和优越性,以现有型号三轴稳定近地卫星控制分系统为实验对象,利用该方法对具有代表性的红外地球敏感器和动量轮的相关遥测数据进行分析;通过将该方法的实验结果与基于Kalman滤波的方法的实验结果进行对比分析,表明该方法能够有效地对卫星的故障进行诊断。     

基于LM-BP神经网络的气阀故障诊断方法

提出一种基于LM（Levenberg-Marquardt）算法优化的 BP (Back Propagation)神经网络的多级往复式压缩机压缩机气阀故障诊断方法。以6M25-185/314氢氮气压缩机的 6级压差和6级温差作为网络的输入向量,建立可对往复式压缩机一至六级气阀故障进行在线监测及故障诊断的LM-BP神经网络模型。以100组故障数据作为网络训练样本,30组数据作为网络检测样本进行故障诊断,结果表明,LM-BP神经网络相比于变梯度BP神经网络和RBF神经网络诊断更快速稳定且准确率达到96%以上。利用Matlab软件平台建立的LM-BP 神经网络故障诊断模型,模型简单便于在工程实际中应用。     

基于元胞储存的小波聚类转子故障诊断

针对传统小波聚类高维空间复杂度高,提出基于元胞储存的改进小波聚类算法;通过对转子故障模拟实验采集的数据,对其聚类分析,结果表明:元胞储存能够降低算法的空间复杂度,提高算法的效率;将改进的小波聚类算法应用于转子故障诊断,与常见的几种转子故障诊断方法相比,结果表明:基于元胞储存的小波聚类转子故障诊断方法具有自动聚类,处理速度快,精度高,适合应用在高维数据空间中等优点,对转子故障状态能够很好地进行区分。     

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.     

基于小波包分解和EMD-SVM的轴承故障诊断方法

针对轴承振动信号具有的非平稳和故障诊断样本数据难以按需获取的问题,设计了一种基于小波包分解和EMD-SVM的故障诊断方法。首先,采用Mallat塔式算法对信号进行降噪,实现信号的小波分解,获得重构后的故障诊断子频带信号。然后,在经典的EMD算法的基础上定义了改进的EMD算法,采用改进的EMD算法对经过小波包降噪的故障诊断子频带信号进行特征提取,从而获得故障诊断特征向量。最后,采用适合小样本分类的SVM进行故障诊断,将经过小波包降噪和EMD特征提取的样本数据用于训练SVM,得到用于故障诊断的多个二分类SVM故障诊断模型,通过投票机制来确定样本数据最终对应的故障诊断类别。在Matlab环境下对轴承故障诊断进行实验,实验结果证明了文中基于小波包和EMD-SVM的方法一种适用于小样本的故障诊断方法,且与其它方法相比,具有诊断效率高和精度高的优点。     

Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph

An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.     

基于邻域粗糙集和并行神经网络的故障诊断

针对目前使用神经网络诊断故障时出现的输入向量选择困难、网络结构复杂、对并发故障诊断效果不好等问题,提出了基于邻域粗糙集和并行神经网络的故障诊断方法。先利用邻域粗糙集对初始征兆进行约简,留下有价值的征兆作为神经网络的输入向量,然后针对每种故障类型设计一个神经网络。用多个训练好的神经网络来并行诊断故障,综合每个神经网络的结果给出最终的诊断结论。用转子实验台的实验数据对这种故障诊断方法进行验证,结果显示该方法能优化神经网络结构,且神经网络具有训练速度快、诊断正确率高的特点。     

Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy

Due to the complicated engineering operation of the check valve in a high−pressure diaphragm pump, its vibration signal tends to show non−stationary and non−linear characteristics. These leads to difficulty extracting fault features and, hence, a low accuracy for fault diagnosis. It is difficult to extract fault features accurately and reliably using the traditional MPE method, and the ELM model has a low accuracy rate in fault classification. Multi−scale weighted permutation entropy (MWPE) is based on extracting multi−scale fault features and arrangement pattern features, and due to the combination of extracting a sequence of amplitude features, fault features are significantly enhanced, which overcomes the deficiency of the single−scale permutation entropy characterizing the complexity of vibration signals. It establishes the check valve fault diagnosis model from the twin extreme learning machine (TELM). The TELM fault diagnosis model established, based on MWPE, aims to find a pair of non−parallel classification hyperplanes in the equipment state space to improve the model’s applicability. Experiments show that the proposed method effectively extracts the characteristics of the vibration signal, and the fault diagnosis model effectively identifies the fault state of the check valve with an accuracy rate of 97.222%.