悬臂式掘进机截割机构状态诊断策略研究

悬臂式掘进机工作中常会遇到煤层硬度突然变大,使截割机构发生异常,甚至导致截齿断裂、减速器齿轮受损、截割电机转子不平衡等故障.为了实现对掘进机截割机构运行状态的远程监控,在分析掘进机截割机构工作原理及重要部件频率特征的基础上,采用Hilbert-Huang分析方法,在MATLAB中仿真得到了四种不同状态下振动信号的频谱特征和能量特征,基于支持向量机算法设计了系统的多故障分类器,通过仿真测试验证了上述分类器能有效地诊断出系统的故障部件,为实现掘进机截割机构运行状态的远程监控奠定了基础.     

一类基于神经网络非线性观测器的鲁棒故障检测

针对一类仿射非线性动态系统,提出了一种基 于神经网络非线性观测器的鲁棒故障检测与隔离的新方法．该方法采用神经网络逼近观测器 系统中的非线性项,提高了状态估计的精度,并从理论上证明了状态估计误差稳定且渐近收 敛到零;另一方面引入神经网络分类器进行故障的模式识别,通过在神经网络输入端加入噪 声项来进行训练,提高神经网络的泛化逼近能力,从而保证对被监测系统的建模误差和外部 扰动具有良好的鲁棒性．最后,利用本文方法针对某型歼击机结构故障进行仿真验证,仿真 结果表明本文方法是有效的．     

近地卫星电源系统能量的仿真分析

该文针对近地轨道卫星,以HXMT天文卫星为研究对象,对其电源系统进行仿真分析。利用轨道设计分析软件STK卫星工具包获取轨道数据以及卫星参数,并基于Simulink技术,着重考虑天文卫星运行过程中姿态变化、不同负载的运行等对电源系统的影响,对卫星的具体能量变化进行计算并建模。最终形成了一个电源系统能量平衡的仿真系统,对电源系统的能量变化给出实时的仿真,并给出了相应的仿真结果。可以判断系统达到平衡的状态,给出蓄电池容量变化,以及充放电等情况的曲线,显示直观效果。     

基于能量演化的航空发动机突发性故障预示模型设计

为减少突发性故障的发生及其对航空发动机的危害,提出了一种用于突发性故障预示的方法,提取系统能量特征,对其运行状态趋势进行预示。建立了突发性故障预示模型,应用能量耗散结构理论对该模型的演化过程与规律进行了描述。利用空停仿真数据对该模型进行了验证,结果表明,该模型具有较好的故障预示能力。     

基于小波与GBDT的无人机传感器故障诊断

相对于有人飞行器,确保无人机传感器的正常工作更为重要.针对无人机传感器的故障诊断,提出了一种将小波特征提取与梯度提升决策树(GBDT)算法相结合的故障诊断方法.采用基于多层小波包分解的特征提取方法,将小波包分解系数与频带能量熵组合构成特征向量,相比单一的能量特征提取方法,有效提升了故障的可分性.采用梯度提升的策略对弱分类器进行迭代优化和线性组合,构成强分类器,使故障分类精度得到显著提高.仿真结果表明,该方法能有效进行特征提取和故障类型识别,且有较高的诊断精度和较强的泛化能力.     

基于小波包—贝叶斯的小电流接地系统故障选线方法

针对现有小电流接地系统故障选线方法存在故障状况复杂、故障信号特征不明显等问题,提出了一种基于小波包-贝叶斯的小电流接地系统故障选线方法。该方法首先按故障过渡电阻值的大小将小电流接地系统故障分为强故障模式、中等故障模式、弱故障模式,并分别构建相应的贝叶斯分类器;在故障特征提取方面,采用db小波包按照能量最大原则选取各条线路的故障特征频带,对所构建的3类分类器进行训练后,再将提取的各条线路的特征频带输入到分类器进行故障类型的判别;最后,采用"少数服从多数"的原则,对3类分类器的输出结果进行表决,进而得出选线结果。仿真结果表明,该方法选线准确率高。     

基于Dymola软件及Modelica语言的飞机供电系统建模与仿真

飞机供电系统的布局设计与控制逻辑是飞机系统设计的重要环节,且其对系统的可靠性、容错供电和余度等特性具有一定影响;对飞机电源系统进行仿真建模研究,在支持各种物理系统建模的Dymola平台下搭建了数字仿真模型,包括交流发电机、变压整流器、电源系统接触器和过欠压、过欠频检测模块等部件模型,并利用各个部件模型搭建了供电系统模型,同时利用Modelica语言完成系统逻辑设计,对整个系统进行仿真;数字仿真结果表明电源系统模型基本能够完成自动配电、自动隔离故障,达到设计要求,为飞机系统设计提供了理论支持。     

基于EEMD和球SVM的高速列车转向架故障估计 

基于监测数据评估高速列车空气弹簧、抗蛇行减振器和横向减振器关键部件的运行状态,针对故障状态下车体构架横向加速度的非平稳信号,提出IMF能量矩与改进的多类超球支持向量机相结合对车体运行状态估计,改进的的超球支持向量机对球外与球内重叠区域的样本用不同的决策,具有更好的分类效果;实验数据仿真分析表明,在速度变化下列车故障识别率稳定在87%以上,证明所用方法能够提取到故障状态下的典型特征,改进的支持向量分类器并能很好的估计出高速列车的故障状态。     

故障树分析法在装甲车辆电源系统故障诊断中的应用

为了能够准确地进行装甲车辆电源系统的故障诊断,深入地研究了故障树分析法在其中的应用;首先,以某型装甲车辆电源系统为研究对象,根据系统失效模型和故障机理,将故障树分析法运用于装甲车辆电源系统故障诊断中,建立电源系统故障树模型,进行故障分析与诊断;其次利用电源系统故障诊断平台对电源系统4个模块进行故障模拟仿真研究,可看出故障树分析法能够准确地诊断出电源系统各个模块的故障,检测精度可达94%,取得了预期效果。     

基于MatLab的列车安全评估仿真平台

以高速列车行走部的运行状态为核心,通过计算机建立以数据管理,状态反演和安全预警为目的高速列车安全性态评估仿真平台。运用Simpack对高速列车的各种故障状态进行实时仿真,并通过Mat Lab对海量数据以小波熵和经验模态分解(EEMD能量距)的方法对高速列车运行过程的故障进行提取特征和分析,以基于向量基和LVQ神经网络的分离器对故障进行识别,从而完成对列车高速列车故障的反演和提前预警。实验表明,上述实验平台能准确地对列车故障状态进行识别,达到安全预警的目的。     

基于压缩感知的RTL级功耗估算仿真

系统芯片功耗动态评估往往需要仿真不同的向量集,估算速度慢;为减少SoC功耗估算时间,结合压缩感知优越的稀疏表示能力,设计一种快速的RTL级功耗估算方案;首先根据芯片RTL描述生成模拟输入矢量,然后利用压缩感知生成原始输入矢量的良好近似表示或精确表示,以减少输入矢量规模,并将其作为新的输入矢量,最后用经压缩的新矢量序列来仿真电路,从而计算出芯片功耗;仿真实验表明,这种功耗估算方法能在保持非常高的精确度的同时,比同类方案缩短仿真时间约28%。     

Fuzzy clustering-based discretization for gene expression classification

This paper presents a novel classification approach that integrates fuzzy class association rules and support vector machines. A fuzzy discretization technique based on fuzzy c-means clustering algorithm is employed to transform the training set, particularly quantitative attributes, to a format appropriate for association rule mining. A hill-climbing procedure is adapted for automatic thresholds adjustment and fuzzy class association rules are mined accordingly. The compatibility between the generated rules and fuzzy patterns is considered to construct a set of feature vectors, which are used to generate a classifier. The reported test results show that compatibility rule-based feature vectors present a highly- qualified source of discrimination knowledge that can substantially impact the prediction power of the final classifier. In order to evaluate the applicability of the proposed method to a variety of domains, it is also utilized for the popular task of gene expression classification. Further, we show how this method provide biologists with an accurate and more understandable classifier model compared to other machine learning techniques.     

Piecewise point classification for uncertainty propagation with nonlinear limit states

Reliability measures the probability that engineered systems successfully perform the intended functionalities under various sources of uncertainties. A piecewise point classification (PPC) method is proposed in this work for effectively propagating uncertainty with nonlinear limit states and approximating the probability of failure accurately. The idea is to efficiently identify a set of points near the critical region, and thus enable capturing the nonlinearity of limit states by constructing piecewise linear approximations. In PPC, the first-order reliability method (FORM) is initially employed to search the most probable point. To handle the nonlinearity of failure surfaces, a sampling-based limit state learning algorithm is then developed to search critical points near the failure surface. With all the points evaluated during the search process, a distance-based piecewise point classification method is developed as a classifier to predict failure events. Monte Carlo simulation (MCS) is finally utilized to propagate uncertainties and approximate the probability of failure, in which a large size of sample points is generated randomly and classified by the developed piecewise point classifier. Three case studies are used to demonstrate the efficacy of the developed approach.     

A CNN-based personalized system for attention detection in wayfinding tasks

Firefighters are often exposed to extensive wayfinding information in various formats owing to the increasing complexity of the built environment. Because of the individual differences in processing assorted types of information, a personalized cognition-driven intelligent system is necessary to reduce the cognitive load and improve the performance in the wayfinding tasks. However, the mixed and multi-dimensional information during the wayfinding tasks bring severe challenges to intelligent systems in detecting and nowcasting the attention of users. In this research, a virtual wayfinding experiment is designed to simulate the human response when subjects are memorizing or recalling different wayfinding information. Convolutional neural networks (CNNs) are designed for automated attention detection based on the power spectrum density of electroencephalography (EEG) data collected during the experiment. The performance of the personalized model and the generalized model are compared and the result shows a personalized CNN is a powerful classifier in detecting the attention of users with high accuracy and efficiency. The study thus will serve a foundation to support the future development of personalized cognition-driven intelligent systems.     

FDBN: Design and development of Fractional Deep Belief Networks for speaker emotion recognition

With an essential demand of human emotional behavior understanding and human machine interaction for the recent electronic applications, speaker emotion recognition is a key component which has attracted a great deal of attention among the researchers. Even though a handful of works are available in the literature for speaker emotion classification, the important challenges such as, distinct emotions, low quality recording, and independent affective states are still need to be addressed with good classifier and discriminative features. Accordingly, a new classifier, called fractional deep belief network (FDBN) is developed by combining deep belief network (DBN) and Fractional Calculus. This new classifier is trained with the multiple features such as tonal power ratio, spectral flux, pitch chroma and Mel frequency cepstral coefficients (MFCC) to make the emotional classes more separable through the spectral characteristics. The proposed FDBN classifier with integrated feature vectors is tested using two databases such as, Berlin database of emotional speech and real time Telugu database. The performance of the proposed FDBN and existing DBN classifiers are validated using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy. The experimental results obtained by the proposed FDBN shows the accuracy of 98.39 and 95.88 % in Berlin and Telugu database.     

基于磨损机理的磨粒图像识别仿真

机械设备磨损过程中产生的磨粒,可以利用智能识别技术进行识别。通过对切削磨粒、球状磨粒、疲劳磨粒以及严重滑动磨粒的磨损机理的研究,提出了能够识别各类磨粒的显著特征,将特征参数进行量化表征,并以特征参数为输入向量,建立支持向量机分类器模型,运用层次法对分类器进行训练,优化分类器的参数,最后利用分类器模型对磨粒图像进行识别以验证识别方法的可行性。实验结果表明,支持向量机分类器识别磨粒类型准确率较高,可以用于磨粒图像的识别。     

Fast intrusion detection based on a non-negative matrix factorization model

In this paper, we present an efficient fast anomaly intrusion detection model incorporating a large amount of data from various data sources. A novel method based on non-negative matrix factorization (NMF) is presented to profile program and user behaviors of a computer system. A large amount of high-dimensional data is collected in our experiments and divided into smaller data blocks by a specific scheme. The system call data is divided into blocks by processes, while command data is divided into consecutive blocks with a fixed length. The frequencies of individual elements in each block of data are computed and placed column by column as data vectors to construct a matrix representation. NMF is employed to reduce the high-dimensional data vectors and anomaly detection can be realized as a very simple classifier in low dimensions. Experimental results show that the model presented in this paper is promising in terms of detection accuracy, computation efficiency and implementation for fast intrusion detection.     

一种基于LabVIEW DSP模块的风机故障诊断系统

设计一种基于图形化开发环境LabVIEW DSP模块的风机故障诊断系统开发方案。方案以32位浮点DSP芯片TMS320C6713为核心,采集风机噪声信号并利用信号的功率谱重心、A声级和小波分解相关频段的能量构成故障诊断的特征向量,以BP网络作为故障的智能分类器,建立起智能诊断系统。实验结果表明,以噪声信号作为诊断对象,采用提升小波和神经网络相融合的诊断与识别技术具有良好的特征提取能力和自适应学习能力,可以准确地识别设备状态。     

基于贝叶斯网络的车辆电源系统故障诊断方法

针对车辆电源系统测试点少且测试数据不完备的问题,提出一种多信号流图模型和贝叶斯网络相结合的故障诊断方法。利用多信号流图模型建立电源系统的故障诊断模型,得到系统故障源与测试信号对应的故障依赖矩阵,在此基础上,建立用于故障诊断的贝叶斯网络结构,根据历史数据完成网络的参数学习,并以故障后验概率最大为准则,实现电源系统的故障诊断。仿真实验验证了该方法的有效性。     

Tool wear state recognition based on linear chain conditional random field model

Tool condition monitoring (TCM) system is paramount for guaranteeing the quality of workpiece and improving the efficiency of the machining process. To overcome the shortcomings of Hidden Markov Model (HMM) and improve the accuracy of tool wear recognition, a linear chain conditional random field (CRF) model is presented. As a global conditional probability model, the main characteristic of this method is that the estimation of the model parameters depends not only on the current feature vectors but also on the context information in the training data. Therefore, it can depict the interrelationship between the feature vectors and the tool wear states accurately. To test the effectiveness of the proposed method, acoustic emission data are collected under four kinds of tool wear state and seven statistical features are selected to realize the tool wear classification by using CRF and hidden Markov model (HMM) based pattern recognition method respectively. Moreover, k-fold cross validation method is utilized to estimate the generation error accurately. The analysis and comparison under different folds schemes show that the CRF model is more accurate for the classification of the tool wear state. Moreover, the stability and the training speed of the CRF classifier outperform the HMM model. This method casts some new lights on the tool wear monitoring especially in the real industrial environment.