基于Peakvue技术的滚动轴承故障诊断

滚动轴承作为旋转机械的关键部件,其运行状态决定设备以及整个系统的性能.滚动轴承出现故障时会产生高频的应力波信号,而Peakvue技术能够有效的检测应力波,运用一种基于Peakvue技术的滚动轴承故障诊断方法.该方法采用加速度传感器采集滚动轴承振动信号,利用高通滤波器滤除加速度传感器输出信号中不必要的低频部分,按照一定的时间间隔对高频信号和应力波信号进行峰值提取,并对提取的峰值信号进行包络检波处理分析故障类型.应用西储大学轴承数据集进行验证,结果表明该方法能准确有效地检测出滚动轴承的故障类型.     

Soft computing and signal processing based active fault tolerant control for benchmark process

Ideally, when faults happen, the closed-loop system should be capable of maintaining its present operation. This leads to the recently studied area of fault-tolerant control (FTC). This paper addresses soft computing and signal processing based active FTC for benchmark process. Design of FTC has three levels: Level 1 comprises a traditional control loop with sensor and actuator interface and the controller. Level 2 comprises the functions of online fault detection and identification. Level 3 comprises the supervisor functionality. Online fault detection and identification has signal processing module, feature extraction module, feature cluster module and fault decision module. Wavelet analysis has been used for signal processing module. In the feature extraction module, feature vector of the sensor faults has been constructed using wavelet analysis, sliding window, absolute maximum value changing ratio and variance changing ratio as a statistical analysis. For the feature cluster module, the self-organizing map (SOM), which is a subtype of artificial neural network has been applied as a classifier of the feature vector. As a benchmark process three-tank system has been used. Control of the three-tank system is provided by fuzzy logic controller. Faults are applied to three level sensors. Sensor faults represent incorrect reading from the sensors that the system is equipped with. When a particular fault occurs in the system, a suitable control scheme has been selected on-line by supervisor functionality to maintain the closed-loop performance of the system. Active FTC has been achieved by switch mode control using fuzzy logic controller. Simulation results show that benchmark process has maintained acceptable performance with FTC for the sensor faults. As a result, when the system has sensor faults soft computing and signal processing based FTC helps for the best performance of the system.     

基于融合分析的感应电机轴承故障检测方法

根据感应电机轴承发生故障时的振动信号特性以及定子电流特性，求出三相电流的Park矢量模信号，并将其与电机滚动轴承振动信号经解调处理后的包络信号进行融合分析。可以从振动信号与电流信号的融合谱图中有效地提取轴承故障特征信息，并将其作为故障识别的依据。实验结果表明，本文检测方法具有较高的信噪比，提高了诊断的可靠性。     

基于加速度传感器和GSM模块的老人跌倒监测系统研制

研制一种基于三轴加速度传感器和GSM模块的老人跌倒检测及远程报警系统。该系统以单片机为核心,实时检测三轴加速度传感器输出信号变化,并配合脉搏传感器检测老人跌倒过程中的心率变化,最终触发GSM模块实现报警短信远传。     

Soft sensor for parameters of mill load based on multi-spectral segments PLS sub-models and on-line adaptive weighted fusion algorithm

The parameters of mill load (ML) not only represent the load of the ball mill, but also determine the grinding production ratio (GPR) of the grinding process. In this paper, a novel soft sensor approach based on multi-spectral segments partial least square (PLS) model and on-line adaptive weighted fusion algorithm is proposed to estimate the ML parameters. At first, frequency spectrums of the shell vibration acceleration signals are obtained. Then the PLS sub-models are constructed with the low, medium and high frequency spectral segments. At last, the PLS sub-models are fused together with a new on-line adaptive weighted fusion algorithm to obtain the final soft sensor models. This soft sensor approach has been successfully applied in a laboratory-scale wet ball mill grinding process.     

Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN

This paper presents a fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert–Huang Transform (HHT) and data mining tool. HHT analyzes the AE signal using intrinsic mode functions (IMFs), which are extracted using the process of Empirical Mode Decomposition (EMD). Instead of time domain approach with Hilbert transform, FFT of IMFs from HHT process are utilized to represent the time frequency domain approach for efficient signal response from rolling element bearing. Further, extracted statistical and acoustic features are used to select proper data mining based fault classifier with or without filter. K-nearest neighbor algorithm is observed to be more efficient classifier with default setting parameters in WEKA. APF-KNN approach, which is based on asymmetric proximity function with optimize feature selection shows better classification accuracy is used. Experimental evaluation for time frequency approach is presented for five bearing conditions such as healthy bearing, bearing with outer race, inner race, ball and combined defect. The experimental results show that the proposed method can increase reliability for the faults diagnosis of ball bearing.     

Analysis and fabrication of a novel MEMS pendulum angular accelerometer with electrostatic actuator feedback

A new type of sensor to directly detect angular acceleration is essential for inertial and control technology. The above interest motivates us to propose a novel micro electromechanical system (MEMS) pendulum angular accelerometer with electrostatic actuator feedback. It adopts a proof pendulum with optimized moment of inertia, suspended to dual anchors by a pair of torsion spring beams, as sensing component. A pair of electrodes are designed as differential capacitors to detect the torsional angular of pendulum, then measure input angular acceleration in sensing axis. Another pair of electrodes are designed as electrostatic actuators for feedback control loop. The structure and operating principle of the MEMS angular accelerometer are introduced. Then, the structure kinetics analysis and signal detecting scheme based on differential capacitors are provided in detail, and the sensitivity and resolution of sensor are derived. Compared with the other MEMS angular accelerometers, the proof pendulum with optimized moment of inertia improves sensitivity and resolution of sensor. The electrostatic actuators feedback loop optimizes the dynamic capability and nonlinearity characteristic. The sensor is fabricated by MEMS fabrication technology. The ANSYS simulation and test results prove the validity of the theoretical analyses. The MEMS angular accelerometer can be used in industrial robots and aircraft by further implementing the signal processing electrocircuit.     

基于主元分析的支持向量数据描述机械故障诊断

针对机械故障诊断缺乏故障样本的问题,提出了故障诊断单值分类法--支持向量数据描述法(SVDD).这种方法只需要正常运行状态的数据样本,就可以建立单值分类器,区分出正常和异常状态.试验以轴承为研究对象,采用主元分析法(PCA)作数据前处理,提取振动信号的统计特征值,得到的主元特征指标输入到SVDD分类器进行训练和测试.试验结果表明,PCA对正常和故障样本有较大的区分度,SVDD分类器能很好的分辨出轴承正常和故障状态,并且对未知故障有良好的识别能力.     

协同训练算法在滚动轴承故障诊断中的应用

针对单个分类器方法在滚动轴承故障诊断中精度较低、故障样本标记稀缺、特征空间维度高等问题，提出一种将协同训练与集成学习相结合的Co-Forest轴承故障诊断算法。Co-Forest是半监督学习中的协同训练算法，包含多个基分类器，通过投票实现协同训练中的置信度估算。从滚动轴承的振动信号中提取时域、频域特征指标。利用少量带标签和大量未标记样本重复地训练基分类器。集成基分类器，实现对滚动轴承故障的诊断。实验结果表明，与同类型的协同训练算法（Co-Training、Tri-Training）相比，Co-Forest算法在轴承故障诊断中具有更高的正确率，与当前针对特征向量高维、标记样本稀缺问题的ISS-LPP算法，SS-LLTSA算法相比，Co-Forest算法在保持很高诊断正确率的情况下，不需要降维、参数设置简单，具有一定的实际应用价值。     

两轮自平衡智能车系统设计

针对智能汽车竞赛中的电磁组参赛要求,提出了两轮自平衡智能车系统的设计方案。主控芯片采用飞思卡尔公司的MC9S12XS128,选用加速度传感器检测车模的倾角,陀螺仪检测车模的角加速度;通过控制两个电机的加减速实现车模的自平衡控制。阐述了卡尔曼滤波法在陀螺仪和加速度传感器信号融合方面的应用,提出了针对闭环速度控制的PI算法。实验表明：该处理方法实现简单,能够准确、快速地实现车模的自平衡控制。     

一种直升机旋翼振动测试方法及实验

为了精确测量直升机旋翼振动特性,设计了一种基于加速度计的直升机旋翼振动测试装置.该装置主要包括加速度传感器、A/D转换模块、无线模块、软件处理等部分.通过无线装置将加速度计采集到的振动信号发送到电脑端,进行数据采集和处理.还进行了标定实验和转台模拟实验,得到了需要的振动特性(振幅、振动频率).测试结果表明,设计符合振动测试要求.     

小波变换在传感器信号滤波中的应用

在流速测量过程中采集的加速度传感器信号是含有随机干扰的信号,为了改善滤波效果,尽可能排除随机信号的干扰,本文介绍了小波变换理论在加速度信息滤波中的应用,并进行了仿真实验。仿真结果表明,小波变换具有良好的时频特性,能有效检测出信号中所含各频率成分,减小测量误差,在传感器信号滤波方面有广阔的应用前景。     

基于小波包和GBDT的瓦斯传感器故障诊断

针对瓦斯传感器的常见故障类型,提出了一种基于小波包和GBDT的瓦斯传感器故障诊断方法.该方法首先使用3层小波包分解对瓦斯原始故障信号进行分解;然后利用LDB算法削减得到重构信号能量,经归一化处理后作为输入分类器的特征向量;接着利用由梯度提升技巧和决策树构成的GBDT分类器作为故障模式的训练和识别器;最后通过瓦斯传感器诊断实例验证了该方法的有效性.实验结果表明,采用该方法进行瓦斯传感器故障诊断相比其他方法具有更高的诊断精度和更好的样本泛化能力.     

基于MEMS惯性传感器的行人航位推算系统

提出一种基于低成本MEMS自包含传感器,能自主完成数据采集、数据处理的行人航位推算（PDR）系统.硬件平台集成三轴加速度计、三轴陀螺仪、三轴数字罗盘及气压计,不需任何额外设施.通过自包含传感器测量行人行走的步长、方位及高度,实现行人室内、外三维定位.采用加速度信号实现跨步探测和步长估计.利用互补滤波器融合加速度计、陀螺仪和数字罗盘数据,矫正陀螺仪的测量误差和磁场干扰对数字罗盘的影响,提高行人行走的方位精度.测试结果表明：系统的定位误差低于行进距离的4％,满足行人定位要求.验证了系统的有效性和可靠性.     

基于压缩采集与CNN的风电机轴承故障诊断

针对传统风电机轴承故障检测存在的采样数据量大、故障特征依赖主观选取的问题,提出了风电机轴承故障的信号压缩采集、自动提取特征及故障诊断的方法,解决了风电机轴承振动信号特征提取计算复杂、受先验知识影响较大的问题。首先基于梯度加速法(NAG)和QR分解理论对随机高斯观测矩阵进行优化,实现风电机轴承振动信号压缩采集;然后将压缩采集得到的数据作为卷积神经网络(CNN)的输入,利用卷积池化层提取压缩采集数据中的故障特征;最后,将得到的故障特征通过softmax分类器进行分类。仿真实验表明:该方法能够自动提取风电机轴承的故障特征,在保证较高故障诊断准确率的同时,缩短了网络训练时间。     

基于SVM的滚动轴承工作状态检测

支持向量机是以统计学习理论为基础发展起来的新的通用学习方法,较好地解决了小样本、高维数、非线性等学习问题.本文提出了一种基于多级支持向量机分类器的滚动轴承工作状态识别方法.该方法通过时域特征参数对原信号进行特征提取,不仅计算简单,而且不考虑滚动轴承的型号和转速.试验表明这种方法具有很好的分类能力.     

改进随机森林及其在人体姿态识别中的应用

针对随机森林算法静态性、容易陷入局部最优等问题,提出了一种蜜蜂交配优化的随机森林算法,并将该算法应用于基于加速度传感器的人体姿态识别。设计了一套以三轴加速度传感器MMA7260与无线通信模块CC2430相结合的数据采集系统,采集了五种日常行为和一种异常行为;从加速度值中提取了近斜率、前后差、均值、均方根和信号幅值面积5类特征矢量;采用改进的随机森林算法训练行为模型和进行分类识别。实验结果表明：该算法能有效地识别六种行为,具有较高的分类预测准确率和行为识别率,且具有较强的稳定性、鲁棒性、全局寻优和抗噪声能力。     

煤矿带式输送机轴承监测诊断系统及其应用

针对煤矿现有带式输送机轴承故障诊断存在的问题,提出了一种新型的煤矿带式输送机轴承监测诊断系统的设计方案。该系统以矿用本安型振动监测分析仪与矿用本安型振动加速度传感器为核心,结合数据库服务器与组态王上位机软件,实现了煤矿带式输送机轴承的振动、温度信号采集与处理、故障实时分析与智能诊断、趋势分析、报告生成等功能。实际应用表明,该系统可实时监测煤矿带式输送机轴承的运行状态并进行故障诊断,满足了煤矿带式输送机运行状态监测与在线诊断的实时性和准确性要求。     

基于量测重构投影的加速度计故障检测

基于分布式传感器网络结构,构建了双节点故障检测模型,分析加速度计输出,对加速度计的输出量进行重构,将与陀螺输出量耦合部分补偿掉,简化加速度计的奇偶检测方程。最后对检测方程不能识别的3个及以上加速度计出现故障的情况进行讨论,分析表明:基于量测重构的奇偶方程加速度计检测法对分布式传感器网络中的加速计故障检测有较强的检测能力。     

基于多传感器的爬楼轮椅底盘机构控制策略研究

为了使底盘机构能够准确地辅助轮椅实现爬楼动作,为其安装检测楼梯环境信息的传感器系统,包括检测踏面与踢面距离的超声波模块和红外线模块,以及检测底盘角度的传感器模块.给出了各传感器模块在底盘机构的位置布局,并通过各传感器模块检测获取底盘机构周围环境的楼梯信息;将采集的模拟数据信息,送入主控板数字信号处理器(DSP)进行融合处理,并采用多机串行通信模式将数据通过串行通信接口(SCI)模块传送至上位机,上位机经分析处理后向电机传送命令使其带动执行机构部件完成相应调节动作,实现对轮椅底盘机构的控制;将传感器的采集结果在VC++6.0界面上实时显示.