基于单片机的直流电动机的信号采集系统设计

装甲车辆起动过程中,直流电动机部分容易发生故障,传统的电机诊断方法都是定期制定维修计划,这种检修方式容易造成维修不足、维修过量以及盲目维修的问题,为了深入分析电动机故障发生时的参数信息的变化,构建一套能够采集电机运行参数的系统,对采集电枢电流和振动信号进行时频域分析,能够反映故障发生的特点;该系统以STM32103C8T6为主控芯片,设计了电流、振动信号采集电路,信号调理电路,对A/D转换模块、数据存储模块进行了编程实现,能够对直流电动机的电枢电流和振动信号进行实时采集,并将数据保存到上位机中进行后续的调用处理;通过测量对比直流电动机起动过程轴承部位发生不同故障时的电流和振动信号,利用MATLAB仿真实现时域内的信号显示,并在MATLAB平台中,编程实现了振动信号的时频域分析;仿真结果表明,该采集系统能够准确测量信号,具有成本低,体积小,精度高等优点,能够为故障特征提取提供较好的数据基础。     

基于滑动峭度相关性准则的局部特征尺度分解分量筛选方法

轴承故障振动信号具有非平稳、非线性特征,且可视为多个调幅-调频分量的叠加,单分量的包络蕴含了轴承的故障特征。局部特征尺度分解可将振动信号准确分解为多个内禀尺度分量之和,某些分量能清晰反映轴承的运行状态,根据包络谱可进行故障诊断。为了准确筛选有用分量,提出了基于滑动峭度相关性准则的分量筛选方法。首先,对信号进行局部特征尺度分解,得到若干个内禀尺度分量;然后,对分量和原始信号分别计算滑动峭度,生成时间序列;最后,依据分量滑动峭度序列与原始信号滑动峭度序列的互相关系数筛选有用分量。通过轴承内圈故障数据分析发现：有用分量与非有用分量之间的滑动峭度互相关系数比互相关系数差异明显,区分度更大,有益于分量的分类、筛选。     

基于LabVIEW的亥姆霍兹线圈磁场自动测量系统

用LabVIEW软件、霍尔传感器、步进电机、继电器、单片机采集系统,构建了亥姆霍兹线圈磁场的自动测量系统.以PC作为上位机,LabVIEW程序控制PC与单片机通信,单片机接收到指令,控制步进电机工作,带动霍尔传感器,传感器采集信号送到单片机,再送到上位机,用LabVIEW进行数据处理和显示,从而实现了亥姆零兹线圈磁场的自动测量.     

基于DSP的嵌入式在线振动信号分析系统

为监测机械设备的工作状态,对机械设备工作过程中产生的振动信号进行采集、处理和分析,从而实现系统的状态监测、故障诊断以及寿命预测等。但目前振动信号分析系统体积较大、不方便携带,多用于离线的振动信号处理,难以完成机械设备振动信号的在线实时分析。针对振动信号离线分析系统存在实时性低、体积大等不足,设计了基于TMS320C6713 DSP的嵌入式振动信号采集处理系统,以满足机械设备振动信号采集、处理和分析过程中对采集、处理实时性,系统便携性等需求。详细介绍了系统的软硬件设计原理和方法,利用美国凯斯西储大学的公开轴承测量数据集对系统的各项功能和技术指标进行实验验证。实验结果表明,该系统能够正常工作且可应用于实际工程中。另外,系统支持功能和算法扩展,以满足不同机械设备的振动信号采集、处理和分析需求。     

基于单片机和LabVIEW的无线火灾监测系统设计

设计了一款的RFTR1无线火灾监测报警系统,重点介绍系统的实现过程。系统以单片机STC12C5A60S2为核心控制器,利用LabVIEW开发环境设计上位机的监测界面,上位机通过串行口与单片机通信,从而实现对多路数据的采集与监测。设计并解释系统的硬件连接,对系统作软件设计,解释了LabVIEW监测平台的应用,经测试系统运行正常。系统具有无需布线、对功能变化的适应性强、系统数据及信息显示直观、人机交互界面好、抗干扰能力强、安装调试简便等特点。     

小波包和GA-SVM在轴承故障诊断中的应用

为了解决傅里叶变换难以兼顾信号在时域和频域中的全貌和局部化特征以及支持向量机惩罚参数 和核函数参数 选取的问题,提出了基于小波包和GA-SVM的轴承故障诊断方法。首先通过实验采集多种工况下故障轴承和正常轴承的振动信号,从振动信号中提取能够表征轴承运行状态的时频域特征以及基于小波包分析的特征向量来作为GA-SVM的输入,然后在SVM的基础上,针对SVM的惩罚参数和核函数参数在不同应用场景下的取值难以确定的特性,采用了遗传算法对支持向量机进行参数优化的GA-SVM算法进行模式识别。实验结果显示,基于小波包和GA-SVM的轴承故障诊断方法比SVM和BP都具有更高的识别精度。     

航空物流传送设备中轴承故障稀疏特征提取

航空物流传送设备中的轴承由于长期受外侵灰尘影响,其内外环极易发生故障;利用计算机采集轴承的振动信号并进行故障特征提取是轴承故障诊断的常用方法;提出了基于稀疏分解的轴承故障特征提取方法;首先,分析轴承故障特征稀疏提取原理;然后,构造参数化Gabor字典,利用遗传算法对故障特征成分进行匹配追踪 (Matching Pursuit,简称MP),以峭度值最大原则作为迭代结束条件;最后,重构提取的特征成分,进行包络谱分析,得出故障类型;对仿真数据和轴承振动数据的测试结果表明,所提方法能有效提取轴承故障特征成分,为航空物流传送设备中轴承的故障监测提供了一种有效方法。     

基于LabVIEW的USB设备无线测试系统设计

针对传统有线USB设备测试系统存在布线、单目标、通用性差等缺陷,设计一套基于LabVIEW的USB设备无线测试系统;系统以LabVIEW为软件开发平台,进行虚拟仪器前面板设计与后台程序的编写;利用无线数据收发模块及其余硬件设备,搭建上位机与设备间无线数据通讯的桥梁;以宏晶STC12C5A60S2单片机为核心控制器,通过引入USB总线接口芯片CH375扩展了USBHOST功能,从而实现上位机和USB设备间数据的无线采集与传输;本系统主要应用于对USB设备的开发调试和测试过程。     

飞机电缆屏蔽层接地可靠性测试系统设计

飞机电缆屏蔽层可靠接地对飞机建立完整屏蔽环路,防止复杂电磁场对飞机内部电路产生干扰具有重要意义;因此,接地可靠性测试尤为必要;测试系统以NI CompactRIO机箱为核心,通过连接上位机、辅助电路以及测试工具搭建硬件测试平台,利用LabVIEW可视化编程语言编写控制程序;上位机程序,通过状态机实现人工交互以及系统整体逻辑控制;FPGA控制程序,用于驱动硬件完成信号的产生和采集工作;实时处理器程序,采用DMA技术与FPGA进行通讯,对辅助电路反馈的采集数据进行快速傅里叶变换提取有效信号,随后应用矢量电压电流法求解电缆屏蔽层环路阻抗;实际测量结果证明:基于LabVIEW的飞机电缆屏蔽层接地可靠性测试系统能够以较高精度完成电缆屏蔽层接地可靠性测试。     

利用LabVIEW改进弦振动实验仪

对传统弦振动实验进行改进,利用LabVIEW的图形化程序设计及数据采集卡的LabVIEW函数调用,编写LabVIEW程序控制采集弦振动信号的数据采集卡,实现弦振动信号的计算机显示,并通过编写LabVIEW程序进行傅里叶变换频谱分析.     

Simulation and analysis of machinery fault signals

Machinery condition monitoring is rapidly finding applications in all branches of industry. In particular, vibration monitoring is playing an increasingly important role as a tool for assisting with predictive and preventive maintenance and for improving operation efficiency of plant. Condition monitoring systems are used for the detection of incipient failure and the diagnosis of the nature of faults in operating machinery. However, for these systems to be reliable an improved understanding is required of the vibration signatures produced by machinery failure mechanisms and of methods for the interpretation of these signals. Many types of fault produce vibration signals which are impulsive in nature and which may be buried in background noise. A method is described for simulating this type of signal and modelling the various stages of incipient failure. Statistical and spectral analysis are used to describe the fault development. The influence of machinery frequency response characteristics on signal transmission from the damaged are to the measurement point are also considered.     

基于复小波包分形理论的爬壁机器人故障检测

通过研究爬壁式机器人的控制和运动特征，提出一种基于复小波包分形理论的故障检测方法．利用复小波包的平移不变性，将爬壁式机器人传感器输出信号分解成独立的频谱，并进行阀值处理和重构，从而有效去除高频噪音并提取故障的特征频率；依据信号分形维数的多尺度不变性，在嵌入维数空间，采用维数最大距离法，确定复小波包域故障信号的关联雏数．仿真实验表明，爬壁式机器人在各种异常工作模式下的故障信号关联维数能比较真实地反映其故障状态空间，同时也验证了故障信号的关联维数低于正常信号的关联维数作为故障发生与否的定量判据的正确性．     

A time domain approach to diagnose gearbox fault based on measured vibration signals

Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.     

滚动轴承声信号特征提取和诊断试验研究

为解决振动检测方法不能有效识别低速旋转机械滚动轴承故障问题,利用声发射检测方法,建立了滚动轴承低速声发射信号采集试验装置,对模拟人工缺陷滚动轴承声发射信号进行了采集,进而对滚动轴承声发射信号进行总体平均经验模式分解,结合能量矩及相关系数法综合判断分解后各模态分量的真伪,据此提取出特征信号并做出其局部Hilbert边际谱,最后对滚动轴承各种故障模式进行诊断。试验结果表明该诊断方法能准确识别滚动轴承声发射信号故障频率,依据特征频率及幅值大小可对低速滚动轴承故障进行有效诊断。     

ARX model-based gearbox fault detection and localization under varying load conditions

The development of the fault detection schemes for gearbox systems has received considerable attention in recent years. Both time series modeling and feature extraction based on wavelet methods have been considered, mostly under constant load. Constant load assumption implies that changes in vibration data are caused only by deterioration of the gearbox. However, most real gearbox systems operate under varying load and speed which affect the vibration signature of the system and in general make it difficult to recognize the occurrence of an impending fault.This paper presents a novel approach to detect and localize the gear failure occurrence for a gearbox operating under varying load conditions. First, residual signal is calculated using an autoregressive model with exogenous variables (ARX) fitted to the time-synchronously averaged (TSA) vibration data and filtered TSA envelopes when the gearbox operated under various load conditions in the healthy state. The gear of interest is divided into several sections so that each section includes the same number of adjacent teeth. Then, the fault detection and localization indicator is calculated by applying F-test to the residual signal of the ARX model. The proposed fault detection scheme indicates not only when the gear fault occurs, but also in which section of the gear. Finally, the performance of the fault detection scheme is checked using full lifetime vibration data obtained from the gearbox operating from a new condition to a breakdown under varying load.     

A wavelet approach to fault diagnosis of a gearbox under varying load conditions

Varying load can cause changes in a measured gearbox vibration signal. However, conventional techniques for fault diagnosis are based on the assumption that changes in vibration signal are only caused by deterioration of the gearbox. There is a need to develop a technique to provide accurate state indicator of gearbox under fluctuating load conditions. This paper presents an approach to gear fault diagnosis based on complex Morlet continuous wavelet transform under this condition. Gear motion residual signal, which represents the departure of time synchronously averaged signal from the average tooth-meshing vibration, is analyzed as source data due to its lower sensitiveness to the alternating load condition. A fault growth parameter based on the amplitude of wavelet transform is proposed to evaluate gear fault advancement quantitatively. We found that this parameter is insensitive to varying load and can correctly indicate early gear fault. For a comparison, the advantages and disadvantages of other measures such as kurtosis, mean, variance, form factor and crest factor, both of residual signal and mean amplitude of continuous wavelet transform waveform, are also discussed. The effectiveness of the proposed fault indicator is demonstrated using a full lifetime vibration data history obtained under sinusoidal varying load.     

Application of correlation matching for automatic bearing fault diagnosis

This paper introduces an automatic feature extraction algorithm for bearing fault diagnosis using correlation filtering-based matching pursuit. This algorithm is described and investigated in theory and practice on both simulated and real bearing vibration signals. First, the vibration model for rolling bearing with fault is derived. Then, the numerical simulation signal being taken as an example, the principle of matching pursuit is mathematically explained and its drawbacks are analyzed. Afterward, to enhance the similarity of model related to the bearing faulty impulses, the model shape parameters are optimized using spectrum kurtosis and smoothing index. After that, the model with optimum shape and period parameters is taken as a template to approximate the impulses in faulty bearing signal. Finally, based on maximizing correlation principle, the optimized cycle parameter being as impuls e repetition period is matched up. The proposed method has been successfully applied in actual vibration signals of rolling element bearing with different faults.     

基于BP神经网络的变速箱故障诊断方法研究

针对变速箱的工作时间不能真实反映实际健康状况的问题,通过提取变速箱的振动信号作为状态参数,建立了基于BP神经网络的变速箱故障诊断模型。该模型首先提取振动信号中对故障反映灵敏的成分作为特征值,获得BP神经网络的训练数据,并通过对比确定最优的隐含层节点数,确定BP神经网络的结构参数。模型训练结束后,以验证数据为例进行故障诊断研究,并对诊断结果进行评估。评估结果表明,该模型准确度高,具有较好的应用和推广价值。     

Induction motor inter turn fault detection using infrared thermographic analysis

Induction motors are the most commonly used prime movers in industries. These are subjected to various environmental, thermal and load stresses that ultimately reduces the motor efficiency and later leads to failure. Inter turn fault is the second most commonly observed faults in the motors and is considered the most severe. It can lead to the failure of complete phase and can even cause accidents, if left undetected or untreated. This paper proposes an online and non invasive technique that uses infrared thermography, in order to detect the presence of inter turn fault in induction motor drive. Two methods have been proposed that detect the fault and estimate its severity. One method uses transient thermal monitoring during the start of motor and other applies pseudo coloring technique on infrared image of the motor, after it reaches a thermal steady state. The designed template for pseudo-coloring is in acquiescence with the InterNational Electrical Testing Association (NETA) thermographic standard. An index is proposed to assess the severity of the fault present in the motor.