基于多信号流模型的电子设备故障诊断方法

为了提高产品的可靠性,并且在电子设备发生故障时迅速地确定故障源,保障设备的正常运行,基于多信号流模型的诊断建模方法,在多信号流基础上引入了以故障重要先验知识为主的多信号流故障诊断策略,通过引入故障模式的故障概率改进了多信号流诊断技术。该方法已应用于BEPCⅡ磁铁电源接口控制设备故障诊断系统,使用TEAMS测试工具箱建模实现了快速准确的故障诊断和定位,提高了磁铁电源控制设备的故障诊断检测率和隔离率,并且可以方便地扩展到其他设备和系统上。     

基于GA-BP的移动通信设备故障诊断

针对通信设备故障发生随机性强,影响因素多,对应的故障诊断有高度非线性和不确定性的特点,采用BP神经网络算法,优化的GA-BP神经网络算法和POS-BP神经网络算法分别搭建基站设备故障诊断模型,提取设备故障历史数据进行MATLAB仿真,准确预测设备故障类型,帮助提高代维公司调度管理的智能化水平,提高基站设备运维的执行效率。仿真结果表明：本文的BP,GA-BP和POS-BP神经网络算法都能够实现设备故障类别的预测,且GA-BP神经网络算法相比BP和POS-BP神经网络算法对通信设备故障诊断有更好的适应性。     

基于融合技术的电子产品PHM系统研究

针对故障预测与健康管理(PHM)的复杂性,对融合技术进行了深入的分析和研究,提出了一种基于神经网络的特征融合方法,融合结果最大限度的给出决策分析所需要的特征信息,提高了故障诊断的可靠性;在预测方法上,提出了一种基于故障预兆监控与推理和失效物理(PoF)模型方法相融合的预测方法,充分利用了每种预测方法的优势,故障预兆监控与推理的方法能够提供故障诊断功能,而失效物理(PoF)模型的方法则有助于确定故障根源,融合预测方法能更加及时准确的预测故障;融合技术丰富了PHM的理论体系,提高了其实用价值。     

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LSSVM的电子装备故障预测研究

针对电子装备的故障信息不足,故障发生率高等特点,通过故障预测有效的监测设备故障状态以及发展趋势,实现对设备的事先维修,避免重大事故的发生,提高电子设备的安全性。对电子装备故障预测进行了分析,提出了一种基于最小二乘支持向量机（LSSVM）的故障预测方法。首先介绍了LSSVM故障预测算法的基本原理和预测流程;然后,对整个电子装备的故障预测研究可以从一个类似的模拟带通滤波器电路故障预测研究出发,将该元件容差设为不同范围来定义电路的不同故障状态,将LSSVM方法与最小二乘法、支持向量机法对电路的不同状态进行预测,可以得到不同状态的预测值,研究结果表明提出的方法能够实现模拟电路的缓变故障预测,且预测效果较好。     

复杂机载电子系统故障综合诊断技术研究

故障综合诊断技术一直是复杂机载电子系统研发过程中的关键部分,当前的故障诊断技术同时需要机内测试（BIT）和场外自动化测试设备（ATE）的测试结果才能得出诊断结果,诊断效率低,时间长并且不能在线诊断。针对新一代战斗机将更加依赖航空电子系统的趋势,迫切需要一种诊断时间短,且能够实现在线诊断的故障诊断技术。因此,一种基于模型的故障诊断方法被提出。该方法通过融合多信号模型和整数编码故障字典模型,模块间采用多信号模型,单个模块中采用整数编码故障字典模型,克服了多信号模型对测试信息的浪费和整数编码故障字典模型建模困难的缺点,并提出一种多目标测试优选方法,通过优化检测方案,充分发挥BIT的检测性能。该方法通过充分使用BIT的测试信息,摆脱了对场外ATE的依赖,实现了在线快速定位故障并识别故障模式。     

基于极限学习机与规则推理的NPC三电平逆变器二级故障诊断方法

针对NPC三电平逆变器故障诊断问题,提出一种基于极限学习机与规则推理的二级故障诊断方法。分析了依据输出电流诊断故障的可区分性,以及故障模式的分类。然后对输出电流提取故障特征,并采用极限学习机完成故障初级分类。对于初级分类结果为电流不可区分故障情况,再根据桥臂电压信息运用规则推理法实现故障二级精确诊断。诊断实验表明,该方法能够实现NPC三电平的多模式故障诊断,且故障诊断方法简单、定位精确、快速、鲁棒性强。     

基于拉普拉斯分值和超球支持向量机的轴承故障诊断方法设计

针对轴承振动信号中的故障信息往往很微弱,同时振动样本数据分布不平衡即故障样本占总样本数的比例低,从而导致故障诊断模型训练不精确而影响诊断精度的问题,提出了一种基于拉普拉斯分值和超球大间隔支持向量机的故障诊断方法。首先,采用有标签的训练样本数据和拉普拉斯分值法提取原始振动信号中的微弱故障信息,并降低其数据维数,从而得到用于故障诊断的特征向量,然后设计了一种改进的超球大间隔支持向量机的故障诊断模型,通过最小化超球体积和最大化超球边界和故障样本之间的间隔来实现故障诊断,以解决样本的不均衡问题,最终通过将测试样本数据代入决策方程并通过投票机制确定其故障类别。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方法能有效解决样本的不均衡情况下的故障诊断,且相对其它方法,具有诊断精度高和收敛速度快的优点。     

基于免疫遗传算法改进的BP神经网络在灭火系统电路板故障诊断中的应用

针对装甲车辆灭火系统电路板规模较大,功能日趋多样与完善的同时,其复杂程度也日益提高,故障层次越来越多,故障现象与故障原因的映射关系更加复杂,组合故障频发,传统的故障诊断方法已不能满足灭火系统电路板故障诊断的要求。设计了基于免疫遗传算法优化的BP神经网络对灭火系统电路板进行故障诊断,并在免疫和遗传过程中保留了部分训练最优解。实现了神经网络收敛速度的提高,使用Matlab编程优化算法并完成了电路板仿真故障的诊断。通过实验验证了该诊断模型的准确性和可靠性,为电气系统通用检测设备的神经网络诊断方法实现提供了理论支撑。     

改进欧拉算法在油液光谱分析趋势预测中的应用

基于原子发射光谱油液分析是大型机械设备磨损状态监测与故障诊断的重要技术,由于灰预测理论在趋势预测方面具有明显的优势,文章利用油液原子发射光谱分析结果,结合灰预测理论,建立了某综合传动油液中金属元素Fe趋势变化的灰预测模型。在模型参数辨识求解上首次引入了改进欧拉算法,解决了避免原灰预测模型在实际应用过程中出现的预测结果主要依赖于第一个实测值的问题,使得预测结果更准确。将该算法结合原子发射光谱分析Fe元素浓度的阈值制定,有效地捕捉到综合传动发生故障的征兆信息,及时采取措施防止综合传动的故障,具有很好的推广应用价值。     

基于无导数优化方法的数值模式误差估计

初始场误差和模式误差是制约数值预报准确率的两个关键因素,本文主要考虑利用历史观测资料实现时空演变的模式误差的估计问题.通过把模式误差综合考虑成为准确模式中的未知项,把历史资料看作是带有未知项的准确模式的特解,构造了求解时空演变的模式误差项的反问题及其最优控制问题.给出了一个解决最优控制问题的无导数优化方法,该方法的优点是不需要建立原数值模式的切线性模式与伴随模式,它只需在增加一个外强迫项的基础上运行原数值模式即可实现模式误差项的最优估计.关于Burgers方程的算例表明,无论模式的初始状态是否准确已知,无导数优化方法都能有效解决时空演变的模式误差的最优估计问题,它为实际业务模式利用历史数据提取模式误差信息并显著地改进预报效果提供了一种方便可行的数值方法与理论依据.     

汛期降水相似动力预报——模式误差主分量相似预报方法

针对相似动力预报中模式预报误差的估计问题,提出将模式误差的直接相似订正问题转化成模式误差主分量的相似预报问题.客观上将模式误差主分量分成可预报和不可预报两部分, 对于可预报主分量采用最优多因子动态配置方案进行相似预报, 而对于不可预报部分则用系统平均代替.基于国家气候中心季节预报业务模式、 美国气候预报中心组合降雨分析资料及国家气候中心气候系统诊断预报室74项环流指数和美国国家海洋和大气管理局的40个气候指数,对东北区域汛期降水进行了预报试验. 2005—2010年6年独立样本检验预报平均距平相关系数为0.29,较系统误差订正预报的0.04有较大提高, 证实该方案能提高国家气候中心季节预报业务模式汛期降水预报水平.     

Improvement of measurement range of optical fiber displacement sensor based on neutral network

By studying the output characteristics of random type optical fiber displacement sensor and semicircular type optical fiber displacement sensor, a sensor of new structure was designed. Using the ratio of the two output signals as the output of the whole system, the measurement range was enlarged, the linearity was improved, and the errors of reflective and absorbent changing in target surface are automatically compensated. Meantime, an optical fiber sensor model of correcting static error and linearizing the output curve based on BP artificial neural network was set up. The intrinsic errors such as fluctuations in the light, circuit excursion, the intensity losses in the fiber lines and the additional losses in the receiving fiber caused by bends were eliminated. By discussing in theory and experiment, the error of nonlinear is 2.9%, the measurement range reaches to 3.0 mm and the relative accuracy is 2%. This kind of sensor offers such advantages as no electromagnetic interference, simple construction, high sensitivity, good accuracy and stability.     

Analogue correction method of errors and its application to numerical weather prediction

In this paper, an analogue correction method of errors (ACE) based on a complicated atmospheric model is further developed and applied to numerical weather prediction (NWP). The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model. Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.     

An approach to estimating and extrapolating model error based on inverse problem methods:towards accurate numerical weather prediction

Model error is one of the key factors restricting the accuracy of numerical weather prediction(NWP). Considering the continuous evolution of the atmosphere, the observed data(ignoring the measurement error) can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers’ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.     

旋转波片偏振仪波片方位误差研究

旋转波片Stokes偏振仪是最常用的测量光束偏振态的仪器。波片快轴方位误差是影响旋转波片Stokes偏振仪的主要误差源之一。为了研究波片方位偏差对测量精度的影响,提出了一种描述波片快轴方位误差向最终的偏振测量误差传递的数学模型,并引入协方差矩阵法表征偏振测量误差。根据这一模型,获得最优的偏振仪配置参数。在推导过程中,假设波片方位误差服从同一高斯分布。基于此误差模型,得到如下结论：(1)由波片方位误差引入的测量误差与光强测量次数N成反比;(2)测量误差独立于入射光强度,但是依赖于入射光偏振态(s₁, s₂, s₃)和波片的位相延迟量δ;(3)波片位相延迟量在(103.22°, 116.13°)范围内时波片方位误差引入的测量误差最小。最后,经过仿真实验证明,所得解析结果与仿真模拟结果相一致。     

HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver.A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band.By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved.These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.     

A method to evaluate error correction ability of computer controlled optical surfacing process

Quantitatively investigating error correction ability in frequency domain is important for computer controlled optical surfacing (CCOS) process to correct different spatial frequency errors. Based on the mathematical model coherence between filtering and material removal process of CCOS, a method is proposed to quantitatively evaluate the correction ability of CCOS process. A generalized model named normalized smoothing spectral function (SSF) will be established combine convolution model of CCOS and power spectral density (PSD) function. A set of polishing experiments are performed to calculate SSF curves and validate SSF model. By comparing the results of SSF curve with PSD curve and surface figure, it reveals that SSF curve can quantitatively indicate the correction ability of CCOS process for different spatial frequency errors.     

激光陀螺捷联惯导的动基座标定

从实际工程应用和维护的角度出发,提出了一种针对舰船在系泊或锚泊条件下激光陀螺捷联惯导安装误差的在线标定方法.该方法依据捷联惯导系统误差方程的基本原理,将标度因数误差、安装误差角、陀螺加速度计常值漂移以及系统基本误差项作为状态变量,用外部提供的高精度经纬度作为观测量,运用Kalman滤波技术估计出激光陀螺和加速度计的常值...     

光学加工过程中高次非球面的三坐标测量数据处理

三坐标轮廓检测是大口径高次非球面确定性加工过程中的主要面形测量手段。由于原始三坐标数据包含较大的检测误差,无法直接应用于加工过程,本文提出了一组数据处理算法对误差进行全面去除。首先,对获取的检测数据采用基于球心曲面重建的测头半径补偿算法进行测头半径误差补偿,然后对补偿后数据进行坐标系旋转平移误差去除,最后对提取的检测面形残差进行基于KNN的残差噪点过滤。其中,提出的基于球心曲面重建的测头半径补偿算法通过引入一个高精度的测头球心包络面拟合模型,来计算各检测点的测头半径补偿向量,仿真实验证明:算法补偿精度达到RMS<4 nm;提出的基于KNN的残差噪点过滤算法,通过采用插值方法提高样本空间密度和优化噪声度量值的计算,提高了噪点的识别敏感度并实现了噪点的自动化去除。最终根据整个误差清理算法构建了检测点云处理软件,应用实践表明其有效提高了镜面加工过程中检测点云的数据处理精度和效率。