高校实验室计算机网络故障诊断技术研究

为了提升高校实验数据处理水平,高校实验室的相关建设工作已经步入正轨,但层出不穷的信息安全问题也向高校实验室计算机网络维护工作提出了新的挑战。文章从现阶段高校实验室计算机网络的相关建设工作出发,简要论述引发网络故障的具体原因,并对其诊断技术进行探讨。     

基于MED和边际谱的自动倾斜器轴承故障诊断

针对直升机自动倾斜器轴承早期微弱故障特征易被强烈背景噪声淹没的问题，提出了一种基于最小熵反褶积（Minimum Entropy Deconvolution，MED）和边际谱的自动倾斜器轴承故障诊断方法。采用MED对采集的振动信号进行滤波降噪，提高了信号的信噪比，突出了轴承早期微弱故障特征；通过Hilbert变换和经验模态分解（Empirical Mode Decomposition，EMD）获取去噪包络信号的本征模态函数（Intrinsic Mode Functions，IMF）集，并引入峭度筛选准则选取合理IMF集计算局部Hilbert边际谱，有效地提取了故障特征频率，能够通过故障特征频率进行故障类型判别。通过某型直升机自动倾斜器故障诊断试验系统验证了该诊断方法的合理性和可行性。     

基于残差决策的自动飞行控制系统远程实时故障诊断

针对自动飞行控制系统结构复杂、关联部件众多,发生故障时诊断时间长,从而影响飞机运行效率的问题,提出一种基于飞机通信寻址报告系统(ACARS)的远程实时故障诊断方案。首先,分析自动飞行控制系统的故障特点,设计搭建检测滤波器;然后,利用ACARS数据链实时发送的自动飞行控制系统的关键信息进行相关部件的残差计算,并根据残差决策算法进行故障诊断及定位;最后,针对不同故障部件残差间的差异大、决策门限无法统一的缺点,提出基于二次差值的残差决策改进算法,减缓了检测对象的整体变化趋势,降低了随机噪声和干扰的影响,避免了将瞬态故障诊断为系统故障的情况。实验仿真结果表明,基于二次差值的改进残差决策算法避免了多决策门限的复杂性,在采样时间为0.1 s的情况下,故障检测所需时间大约为2 s,故障检测时间大幅降低,有效故障检测率大于90%。     

回采巷道过断层施工技术

受断层影响,巷道过断层期间掘进速度缓慢,影响采掘接替和矿井生产效率。为了更好地促进矿井生产,以23上04轨顺掘进为研究对象,采用FLAC 3D软件模拟巷道过断层时巷道围岩变形量和塑性区范围,并将围岩划分为正常围岩、过渡带和破碎带3部分。根据模拟结果可知,围岩变形量和塑性区范围都在过渡带开始增大,在破碎带达到最大值。巷道采用后退卧底法过断层,并对断层破碎带及时采用锚网索、注浆、U型钢棚进行支护,通过联合支护相互配合,可以共同提高巷道支护强度,增强巷道顶帮抗变形能力。     

A novel fault detection method for VSC-HVDC transmission system of offshore wind farm

VSC-HVDC transmission system is going to become the most economical way of power delivery for large and remote offshore wind farms. An accurate and fast fault detection method is necessary to protect sensitive devices of these systems and maintain uninterrupted power delivery. This paper investigates an innovative technique for recognizing DC zone faults including HVDC cable faults and unbalancing of DC capacitor bank. Sheath voltage is presented as a novel criterion for detecting abnormal situations in the system. Transient voltage of cable sheath and Wavelet transform are used to identify different types of DC faults. Extensive simulation examples are performed on EMTDC–PSCAD platform and post-processed using MATLAB. The results illustrate that the proposed technique gives a robust performance and can be applied to protection scheme of offshore wind farms.     

Protection of power transformer using multi criteria decision-making

Power transformers are protected by different relays that operate independently. Malfunction of each relay has a major role in reducing the reliability of the protection system. In order to mitigate the main drawbacks of the power transformer relays, an overall protection scheme is presented in this paper. This scheme proposes a novel multi criterion algorithm using decision-making based on fuzzy logic. In this paper the outputs of restricted earth fault relay and a directional check unit, are combined with the output of the differential protection relay. Therefore, problems that are pertaining to independent operation of each relay have been mitigated and the relays cover protection blind spots of each other. The improved power transformer protection (IPTP) scheme enhances the sensitivity and reliability of the power transformer protection. Extensive simulations are used to measure the effectiveness and merit of the proposed IPTP relay. The above efforts result in a multi criteria approach for protection of power transformers.     

PTA污水厂COD在线监测结果不合格原因分析及对策

对在线自动监测仪监测结果与人工化验数据存在误差的原因进行分析,以提高在线自动监测仪监测结果的科学性和可靠性,并提出了相对应的建议,有效提升装置出水合格率。     

基于小波时频图和卷积神经网络的断路器故障诊断分析

高压断路器操动机构振动信号为非平稳性信号,蕴含着丰富的操动机构工作状态的信息,对操动机构工作状态的检验辨识具有重大意义。提出一种基于小波时频图和卷积神经网络的断路器故障诊断方法。对操动机构振动信号进行连续小波变换生成时频图(CWT),并对时频图进行统一压缩预处理;将预处理后的时频图作为特征图输入卷积神经网络AlexNet模型;通过对网络参数的调整,逐步改进网络模型,有监督地实现对操动机构故障状态的辨识诊断。结果表明,该方法能够有效地运用于断路器操动机构故障辨识诊断,与小波频带能量-RBF、小波频带能量-SVM的故障识别相比,故障识别准确率最高。     

Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs/BPNNs

The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring, and identification of faults are of great significance. At present, the fault research on diesel engines still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a fixed speed. A novel fault detection and diagnostic method of diesel engine by combining rule-based algorithm and Bayesian networks (BNs) or Back Propagation neural networks (BPNNs) is proposed. The signals are processed by wavelet threshold denoising and ensemble empirical mode decomposition. The signal-sensitive feature values are extracted from the decomposed intrinsic mode function. Seven faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs. Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds. In addition, the influences of the layers of decomposed signals, sensor noise and external excitation interference on the fault diagnostic performance are also researched.     

电子电路故障检测技术探索

本文深入探究电子电路障碍检测技术出现故障的原因,对电子电路故障的检测技术实施具体分析。