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。     

Parallel optical fuzzy logic inference using a SLM-based architecture

A new optoelectronic fuzzy inference system is proposed for processing a large number of fuzzy rules in parallel. The proposed system using spatial light modulator implements various membership functions as well as max–min inference. It has the features of easy implementation and large data processing capability. The membership function decomposition method is used to save space bandwidth and accommodate multiple-input fuzzy inference.     

边界扫描测试在数字电路自动测试系统中的研究与应用[BT)]

边界扫描测试是当前数字电路模块故障隔离的主要手段,但测试能力受到模块可测试性设计限制,其它方面作用也被忽略。文中对数字电路自动测试系统设计提出了边界扫描测试应用技术,包括系统自检测试,模块功能测试和模块故障隔离。在系统自检测试中应用边界扫描,提高了设备自检故障隔离精度;而对于模块功能测试,边界扫描测试技术提供了一种新的选择;在故障隔离中扩展和延伸应用边界扫描技术,突破了模块自身测试性的限制,提高了边界扫描测试的故障覆盖率和故障隔离精度。通过实际测试验证表明,该设计方法稳定可靠,同时提升了测试系统自身和模块的测试能力和隔离精度。     

多尺度传感网络失效节点检测系统设计

对多尺度传感网络中的失效节点进行准确检测与定位,实现故障节点的高效检测,保障传感网络的可靠运行。提出一种基于多传感器量化融合跟踪滤波检测的失效节点检测算法,并进行检测系统优化设计。构建多尺度传感网络的节点分布实体对象模型,进行失效节点检测系统总体设计和技术指标分析。设计基于多传感器量化融合跟踪滤波检测的失效节点检测算法。进行系统的硬件设计,包括A/D模块设计、时钟电路设计、程序加载电路设计、传感器通信模块设计和系统电源模块设计。在ARM Cortex?-M0平台上进行检测系统软件开发。系统仿真结果表明,该系统进行多尺度传感网络失效节点检测的准确度较高,提高了传感器网络的寿命周期。     

基于CLIPS的机载光电跟踪设备故障诊断专家系统研究

针对机载光电跟踪设备传感器种类多、单元部件结构复杂和急需快速故障定位的要求,设计了机载光电跟踪设备故障诊断专家系统。通过建立跟踪系统故障树并分析其故障特点,确定了模糊多值产生式规则的知识表示方式和冲突消解策略,构建了专家系统知识库和推理机;同时结合数据库建立的故障表和规则表,实现专家系统解释器功能;最后将CLIPS嵌入VC设计友好人机交互界面。实验表明,该专家系统操作方便,能快速准确定位故障、解释故障成因以及提供维修建议。     

HIAF-BRing电源样机模块故障联锁保护系统设计与实现

强流重离子加速器装置（HIAF）的增强器（BRing）二极铁电源样机采用多模块串并联的全储能快循环脉冲电源实现方案,电源功率达到MW级。由于电源规模庞大和功率巨大,为了在运行中迅速保护电源设备,设计并实现了一套双冗余的基于可编程逻辑控制器（PLC）、模块故障联锁板和现场可编程门阵列（FPGA）的模块故障联锁保护系统,利用硬件和软件同时对电源功率单元模块实施故障检测、故障传递和故障保护。设计完成后分别从电源联锁环路的响应时间、核心控制板故障引发电源环路联锁的总时间和设备故障响应等三个方面进行测试,测试结果表明,在电源发生故障时,模块故障联锁保护系统满足电源样机对实时性和可靠性的要求,达到设计目标。     

基于高压模块供电的MCP-PMT高压击穿故障分析及设计改进北大核心CSCD

为克服MCP-PMT传统高压供电设备体积大、便携性差的问题,采用了基于小型化高压模块的高压供电方式。该系统在实际使用过程中发生了一起MCP-PMT高压击穿故障。详细分析了该系统的故障原因,将问题定位为MCP-PMT内部真空度下降导致的耐压性能下降,同时还发现了导致故障发生的诱因是高压模块在加电时刻存在的高压过冲现象。针对高压模块的过冲问题,本文提出了较为巧妙的设计改进方法并取得了良好的效果,进一步提高了MCP-PMT系统的工程应用可靠性。     

A beam flexure-based nanopositioning stage supporting laser direct-write nanofabrication

A nanopositioning system of both millimetric stroke and nanometric tracking accuracy is a key component for nanofabrication in many applications. In this paper, a novel bi-axial beam-flexure nano servo stage is proposed to support a direct writing system for femtosecond laser nanofabrication. The important features of the stage lie in: a mirror symmetric instead of rotational symmetric configuration is adopted to restrict cross axis coupling, and a novel Z-shaped guidance module is proposed to achieve relative large linear stiffness range, in addition a redundant constraints module is introduced to increase off-axis stiffness of the stage. Mechanical analysis and system identification are provided, with which a feedback control algorithm demonstrates the tracking capability for laser fabrication purposes. Based on the fabricated XY nano-stage, real time control and measurements are deployed, demonstrating the millimetric operating workspace and 77.8 nm (RMS) error of tracking a circular trajectory.     

旋转机械振动故障诊断专家系统

针对旋转机械故障诊断智能化程度不高的现状,采用可视化语言LabVIEW作为故障诊断专家系统前端开发平台,采用MySQL作为系统后台数据库,开发了一个基于产生式规则表示知识的旋转机械故障诊断专家系统。介绍了该系统的总体结构、知识库、推理机的设计。最后给出该专家系统的实例验证,证明了该方法的实用性。     

Asymmetric delay feedback stochastic resonance detection method based on prior knowledge particle swarm optimization

For the adjustable parameters stochastic resonance system, the selection of the structural parameters plays a decisive role in the performance of the detection method. The vibration signal of rotating machinery is non-linear and unstable, and its weak fault characteristics are easily concealed by noise. Under strong background noise interference, the detection of fault features is particularly challenging. Therefore, a type of weak fault feature extraction method, named knowledge-based particle swarm optimization algorithm for asymptotic delayed feedback stochastic resonance (abbreviated as KPSO-ADFSR) is proposed. Through deduction under adiabatic approximation, we observe that both the asymmetric parameters, the length of delay and the feedback strength, impact the potential function. After adjusting the asymmetric parameters of the system, the output signal-to-noise ratio (SNR) is used as the fitness function, and the setting of the relationship between the noise intensity and barrier height is used as the prior knowledge of the particle swarm algorithm. Through this algorithm, the delay length and the feedback strength are optimized. This method achieves global optimization of system parameters in a short time; it overcomes the shortcomings of the traditional stochastic resonance method, which has a long convergence time and tends to easily fall into local optimization. It can effectively improve the detection of weak fault features. In the bearing rolling body pitting corrosion failure experiment and steel field engineering experiment, the proposed method could extract the characteristics of a weak fault more effectively than the traditional stochastic resonance method based on the standard particle swarm algorithm.     

商用车维修服务链关键技术研究

作为构建商用车维修服务链的关键设备,设计了一种基于J1939协议的车载商用车故障诊断系统。该系统扩展了传统车载故障诊断系统,利用无线通讯技术将采集到的J1939车辆故障信息和GPS定位信息实时上传至车辆监控中心。目标是实现商用车车辆动态监控、故障诊断和预警以及车辆维修保养服务的网络协同管理。系统以Cortex-A8为主控制器和嵌入式Linux为平台,包括故障采集与分析模块,利用Qt设计用户界面。经多次测试实验,故障诊断快速准确,无线传输数据实时性强,丢包率低。     

基于多区域代理的分布式故障诊断方法

军用大型复杂装备组成涉及多个子系统或多个装备,故障诊断涉及上百个分散测试点数据采集和分离模块的诊断推理。传统装备采用集中测试和诊断方法,配套测试系统结构设计复杂、系统故障诊断算法效率低。本文基于D-矩阵故障诊断原理,提出基于多区域Agent的分布式故障诊断结构模型和区域Agent数据协同、区域Agent测试结果仲裁等问题的解决办法,并以某型舰船装备为例,验证了基于多区域代理的分布式故障诊断技术的有效性。     

长时语音特征在说话人识别技术上的应用

本文除介绍常用的说话人识别技术外,主要论述了一种基于长时时频特征的说话人识别方法,对输入的语音首先进行VAD处理,得到干净的语音后,对其提取基本时频特征。在每一语音单元内把基频、共振峰、谐波等时频特征的轨迹用Legendre多项式拟合的方法提取出主要的拟合参数,再利用HLDA的技术进行特征降维,用高斯混合模型的均值超向量表示每句话音时频特征的统计信息。在NIST06说话人1side-1side说话人测试集中,取得了18.7%的等错率,与传统的基于MFCC特征的说话人系统进行融合,等错率从4.9%下降到了4.6%,获得了6%的相对等错率下降。      

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.     

Message Passing-Based Inference for Time-Varying Autoregressive Models

Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by a factor graph and solve the inference problem by automated message passing-based inference for states and parameters. We derive structured variational update rules for a composite “AR node” with probabilistic observations that can be used as a plug-in module in hierarchical models, for example, to model the time-varying behavior of the hyper-parameters of a time-varying AR model. Our method includes tracking of variational free energy (FE) as a Bayesian measure of TVAR model performance. The proposed methods are verified on a synthetic data set and validated on real-world data from temperature modeling and speech enhancement tasks.     

基于信息融合的模拟电路故障的特征提取与融合方法

在模拟电路故障诊断中,故障特征的提取是一个非常重要的环节,其提取结果的好坏将直接影响最终的诊断正确率。对现有文献研究发现,每种特征提取方法单独使用时都有一定的局限性,为了能够更加充分的提取模拟电路故障特征,提出了小波包分析与主元分析并行应用的方法,并将两种方法提取的特征向量依据不同规则进行了三种类型的融合,方便对比实验。为获取最优小波特征,提出了特征偏离度,并以此为标准选择最优小波基。最后,通过设计一种改进的神经网络分类器模型,将融合后的三种特征向量送入其中进行仿真验证,得出最终诊断结果。结果表明,该方法能够有效克服单一特征提取方法提取不充分的缺点,提高故障诊断的正确率,并且融合因子 适中时诊断正确率最高。     

基于IEEE1232的模拟电路TPS自动生成系统开发

目前在模拟电路故障诊断及测试过程中存在两个问题：测试信号的连续性及容差特性造成的测试信号数量巨大,故障知识表示复杂,测试程序（Test Program,简称TP）的编写多用基于决策知识的人工生成方法。通过对IEEE1232标准的体系结构和诊断推理机要求的分析,论文对IEEEE1232模型体系进行扩充,提出一种包含特征提取技术和多种AI诊断方法的诊断知识库生成协议,设计并实现了符合1232标准知识库的TPS自动生成测试系统。提高了诊断知识的移植性,实现了TPS的自动生成。仿真结果证明了该方案的可行性     

EEMD与EFICA在单通道复合故障诊断中的应用

针对转子不对中和滚动轴承微弱损伤的复合故障诊断问题,提出了一种基于平均经验算法(Ensemble Empirical Mode Decomposition,EEMD)和高效快速独立分量分析(Efficient Variant of FastICA,EFICA)的盲源分离故障诊断方法。利用EEMD算法将单通路复合故障信号分解成多个不同信号特征的本征模函数(Intrinsic Mode Function,IMF),解决了盲源分离中的欠定问题。在此基础上利用EFICA算法对各个不同信号特征的IMF进行故障特征分离。通过仿真实验和转子实验台的实验结果,表明该算法可以有效分离出各个不同的故障特征。     

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.