An order-tracking technique for the diagnosis of faults in rotating machineries using a variable step-size affine projection algorithm

This paper describes principles and applications of an adaptive order tracking diagnosis technique for fault diagnosis in rotating machinery. An adaptive high-resolution order tracking method with a variable step-size affine-projection algorithm (VSS APA) is used to diagnose faults in the gear-set and centrifugal fan blades. In comparison with conventional order-tracking methods such as recursive least-square filtering algorithm, the proposed VSS APA technique has fast convergence speed for adaptive filtering process. The VSS APA-based order-tracking technique can overcome problems encountered in FFT based methods. The smearing problem is treated as the tracking of frequency-varying band-pass signals. Ordered amplitudes can be calculated with high-resolution adaptive filter algorithm after experimental implementations carried out to evaluate the proposed algorithm in gear-set and centrifugal fan blades defect diagnosis. The experimental result indicates that the proposed algorithm is effective in rotating machinery fault diagnosis.     

Design of an adaptive line enhancement system using a variable step-size affine-projection algorithm

This paper presents design of an adaptive line enhancement (ALE) system for improving sensor response using a variable step-size affine-projection algorithm (VSS APA). ALE is an adaptive technique that may be used to detect a periodic signal buried in a broadband noise background such as in rotating machinery fault diagnosis. However, most of the conventional methods for ALE system are based primarily on an adaptive filter with the least-mean-square (LMS) error algorithm. Unfortunately, convergence speed is limited when a filtering plant is varied, because the learning process of the adaptive algorithm fails to respond quickly enough to the changing operational conditions. This study proposed a VSS APA for improving both the convergence speed and the performance of the ALE system. Two applications were conducted to compare the performance of the proposed algorithm and various traditional adaptive filtering algorithms. The first application used the proposed ALE system to improve the response of a wheel speed sensor output signal; the other was used for reducing the background noise during rotating machinery fault diagnosis. Both the experimental results indicated that the ALE with VSS APA has an effective performance and convergence for both applications.     

基于SVMD和自适应MOMEDA的齿轮箱故障诊断

受背景噪声和传输路径的影响，故障信号往往被淹没，故障特征难以提取。基于此，提出一种连续变分模态分解(SVMD)和自适应MOMEDA相结合的故障诊断方法，通过SVMD前处理得到重构信号，然后以平均谱负熵为适应函数，通过人工鱼群优化算法自适应选择MOMEDA的最优参数。利用所得参数对重构信号进行MOMEDA滤波，最后进行包络谱分析，做出故障类型诊断。将所提方法应用于齿轮箱主动轮断齿故障的仿真信号和实验信号中，在包络频谱中可以清楚地分辨出小齿轮转频及其倍频， 同时所提方法相对其他方法具有更好的表现效果。     

Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for fault diagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and vibration signals already exists in the form of techniques applying the time and frequency domain of signals, and analyzing the difference of signals in the spectrum. Unfortunately, in some applications the performance is limited, such as when a smearing problem arises at various rates of engine revolution, or when the signals caused by a damaged element are buried in broadband background noise. In the present study, a continuous wavelet transform technique for the fault signal diagnosis is proposed. In the experimental work, the proposed continuous wavelet algorithm was used for fault signal diagnosis in an internal combustion engine and its cooling system. The experimental results indicated that the proposed continuous wavelet transform technique is effective in fault signal diagnosis for both experimental cases. Furthermore, a characteristic analysis and experimental comparison of the vibration signal and acoustic emission signal analysis with the proposed algorithm are also presented in this report.     

Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals

An investigation of the fault diagnosis technique in internal combustion engines based on the visual dot pattern of acoustic and vibration signals is presented in this paper. Acoustic emissions and vibration signals are well known as being able to be used for monitoring the conditions of rotating machineries. Most of the conventional methods for fault diagnosis using acoustic and vibration signals are primarily based on observing the amplitude differences in the time or frequency domain. Unfortunately, the signals caused by damaged elements, such as those buried in broadband background noise or from smearing problems arising in practical applications, particularly at low revolution, are not always available. In the present study, a visual dot pattern technique is proposed to identify the acoustic emission and vibration signals for fault diagnosis in an internal combustion engine and drive axle shaft. Experiments are carried out to evaluate the proposed system for fault diagnosis under various fault conditions. The experimental results indicate that the proposed technique is effective in the fault diagnosis of an internal combustion engine and drive axle shaft.     

基于卡尔曼滤波的区间式铝电解槽似在电阻采集算法

针对目前铝电解行业对于槽似在电阻的采集不够准确并且延时较高的问题,本文提出一种基于卡尔曼滤波的区间式槽似在电阻采集算法.该算法以卡尔曼滤波为基础,用预测值与采样值的均方差表征它们的高斯白噪声功率,使其能够在电阻平稳的状态下有着较强的跟踪性能;再结合一阶惯性滤波的强滤波特性和卡尔曼滤波的强跟踪优势,设置适用的滤波区间,确...     

多新息理论优化卡尔曼滤波焊缝在线识别

针对间隙小于0.05 mm的低碳钢对接焊缝,用磁光传感方法获取焊缝位置信息,研究多新息理论优化卡尔曼滤波在焊缝识别及跟踪中的应用.在获取磁光图像及提取焊缝位置的过程中存在较多干扰,而传统卡尔曼滤波受噪声的影响较大,难以对焊缝偏差进行最优估计.为此,结合多新息理论,提出一种焊缝位置检测的卡尔曼滤波改进算法,在对当前时刻进行预测时,充分考虑之前多个时刻的运动状态,综合历史数据估计出焊缝位置信息,对不同新息值进行试验比较并考虑计算量和滤波精度,发现选用两个新息值优化卡尔曼滤波算法可得到较好的效果.结果表明,多信息理论优化卡尔曼滤波算法可有效提高焊缝位置检测精度.     

改进非线性自适应卡尔曼滤波器滤波效果分析

为解决机械系统特别是航空液压管路系统振动过程中存在诸多噪声干扰、难以保证对有效振动信号进行准确分析的问题,结合非线性自适应算法、最小二乘法及传统卡尔曼滤波器,设计改进非线性自适应卡尔曼滤波器。通过仿真,在模拟的振动信号中加入随机噪声,并且将滤波前后振动信号的时域图和频域图进行对比。通过实验数据进行滤波效果对比,验证非线性自适应卡尔曼滤波器滤波效果的优越性。     

基于卡尔曼滤波的焊缝偏差实时最优估计

建立了基于卡尔曼滤波的焊缝偏差实时最优估计算法.以焊缝中心位置为特征矢量,建立焊缝位置检测的状态方程和测量方程,并依据最小均方差原则建立了卡尔曼滤波最优估计的递推算法.测量噪声协方差由传感器测量误差的统计值得到,假定过程噪声是由于加速度变化引入,通过两点法确定焊缝中心位置的初值.在焊接过程中,应用卡尔曼滤波消除噪声干扰,实现焊缝位置的实时精确预测.计算机仿真和试验结果表明,焊缝偏差信号经过卡尔曼滤波处理后,消除了偶然因素和随机噪声的影响,提高了跟踪精度以及系统工作的稳定性,适合实际工程应用.

Abstract：

The optimal estimation algorithm for real-time welding deviation based on Kalman filtering is presented. The state equation and measurement equation for detecting the weld position is established, and the optimal estimation of the Kalman filtering recursive algorithm also is established according to the principle of minimum mean square error. Measurement noise covariance is obtained from the statistical value of measurement error, and after the process noise is supposed to derive from the changes in acceleration, the initial values of the welding center position are determined by the twopoint method. During the welding process, the welding position is accurately predicted while the noise interference is eliminated by Kalman filtering. The computer simulation and experiment results show that the weld deviation signal processed by the Kalman filtering can eliminate the disturbance of causal factors and random noise,improve the tracking precision and the stability of system, and be suitable for the practical engineering applications.     

微间隙焊缝磁光成像NN-KF跟踪算法分析

针对紧密对接微间隙焊缝,分析基于磁光成像的神经网络补偿卡尔曼滤波（kalman filtering compensated by neural network,NN-KF）跟踪算法,建立焊缝位置测量模型并运用卡尔曼滤波对焊缝位置偏差进行最优预测.卡尔曼滤波进行最优估计需建立准确的系统模型和观测模型,而在焊缝跟踪过程中,系统噪声具有非先验性.对于针对测量模型误差、过程噪声和测量噪声对卡尔曼滤波结果的影响,运用反向传播（back propagation,BP）神经网络对卡尔曼滤波结果进行修正,补偿模型误差及噪声统计不确定性造成的滤波误差.结果表明,BP神经网络补偿卡尔曼滤波算法能有效抑制滤波发散,减小噪声干扰影响,提高焊缝跟踪精度.     

大数据框架下基于改进自适应滤波算法的机械故障信号处理

针对经典自适应滤波算法处理机械故障信号时收敛过慢的问题,在大数据框架下提出一种改进的自适应滤波算法.以Hadoop平台为基础架构,构建一种三层次结构的机械故障大数据处理框架,用于采集和预处理原始故障大数据集;在信号滤波方面引入步长变化因子函数和均方误差函数,提高算法的收敛性能;基于离散粒子群算法对故障信号滤波处理过程进...     

磁光成像自适应卡尔曼滤波焊缝跟踪算法

焊缝跟踪是保证焊接质量的前提.针对0~0.05 mm的微间隙焊缝,研究一种色噪声环境下应用卡尔曼滤波实现焊缝跟踪的方法.通过对焊件施加磁场,利用法拉第磁旋光原理构成磁光传感器并获取焊缝磁光图像,提取焊缝中心位置构成状态向量,建立基于焊缝中心位置的系统状态方程与测量方程.针对系统过程噪声为色噪声,使用Sage自适应卡尔曼滤波,采用新息序列估计过程噪声协方差矩阵,准确预测焊缝中心位置.结果表明,根据自适应卡尔曼滤波方法能够有效提高焊缝跟踪精度.     

多特征联合时空正则化相关滤波目标跟踪鲁棒算法

针对相关滤波跟踪框架中快速运动带来的边界效应和遮挡情况下模型错误学习的问题,提出多特征联合时空正则化的相关滤波目标跟踪算法。算法在第一帧提取目标区域的快速方向梯度直方图特征、颜色空间特征和深度卷积特征,并使用主成分分析法降低特征的维度;然后在相关滤波跟踪框架中加入空域和时域正则化项,来缓解跟踪过程中边界效应和模型退化等问题;最后结合尺度池方法,对跟踪目标进行自适应的尺度估计。实验结果表明,该算法在目标发生尺度变化、遮挡、快速运动等情况下,仍具有较好的跟踪有效性。     

基于正交匹配追踪算法的齿轮箱轴承故障诊断及实验验证

为了提高传动振动过程中齿轮箱轴承内外圈故障诊断能力,采用正交匹配追踪(OMP)算法建立了故障诊断模型,并开展仿真分析及实验验证。研究结果表明：通过OMP算法对轴承外圈故障仿真加噪信号进行处理,能够看到信号呈周期性波动,通过频率及其倍频呈现逐步衰减,故障特征明显;经过OMP算法处理的轴承内圈故障仿真纯净信号呈周期性波动,能够看到滚动轴承的故障,轴承内圈通过频率和倍频以及边频带呈现逐步衰减,故障特征明显。为了进一步验证OMP算法处理齿轮故障的有效性,搭建封闭式功率流齿轮箱试验台,OMP重构故障信号谱图中啮合频率360 Hz峰值较低,边频带被完全掩盖,不存在大量的干扰成分。经OMP算法处理过的故障信号的谱图能很好地体现故障特征。     

基于GWO-CMFH和改进ResNet轴承故障诊断

针对不同程度的小分类轴承故障，现有故障诊断方法准确率不高的问题，提出基于GWO-CMFH和改进ResNet的滚动轴承故障诊断方法。对于同一类型不同程度故障，提出基于GWO自适应优化结构元素参数的CMFH滤波方法，增强振动信号的脉冲故障特征并抑制背景噪声；采用连续小波变换将滤波后的信号转换成二维时频图谱；最后，提出基于混合注意力机制改进的残差网络模型，提高轴承故障诊断精度。在西储大学、东南大学及所选轴承数据集上进行验证实验，不同故障程度的小分类诊断准确率分别达到99.73%、98.12%和99.07%,表明所提方法具有很好的抗噪性、鲁棒性，可提高滚动轴承不同故障程度的诊断效果。     

基于SSA-VMD和ITFPF的喷油器信号去噪方法

由于柴油机工作环境复杂多变,喷油器信号中包含较多噪声,导致诊断率低。为降低噪声的影响,提高故障诊断率,提出一种麻雀算法优化变分模态分解（SSA-VMD）和改进时频峰值滤波（ITFPF)结合的信号去噪方法。针对VMD受分解参数制约的问题,以能量分解因子为目标函数,通过SSA自适应地将信号分解成一系列IMF,以解决参数设置不当导致的模态混叠问题。TFPF的窗长选择不当会影响其滤波效果,由于排列熵能够度量非平稳信号的复杂度,以排列熵为适应度函数寻到最优窗长,使ITFPF兼顾信号保真和噪声压制。仿真和试验结果表明：SSA-VMD和ITFPF结合的方法降噪效果优于其他方法,并使故障识别率相比未优化VMD提高了10.13%。     

基于集中式卡尔曼滤波干扰观测器的无模型自适应控制

针对一类具有测量扰动的离散时间非线性系统,提出一种基于集中式卡尔曼滤波干扰观测器的无模型自适应控制方法。利用动态线性化方法构造被控系统的线性化数据模型;根据线性化数据模型和传感器的测量数据,设计最优集中式卡尔曼滤波干扰观测器;并利用观测器的输出在线调整伪偏导数,提出系统的控制更新方案。该方案的设计和分析不依赖于除输入输出数据的任何模型信息,可避免常规无模型自适应控制方法容易受测量扰动的影响。仿真结果表明：与基于单个传感器卡尔曼滤波干扰观测器的无模型自适应控制方法相比,提出的基于多传感器最优集中式卡尔曼滤波干扰观测器的无模型自适应控制方法具有更好的跟踪性能和更大的数据信噪比。     

Seam tracking based on estimation of weld position using Kalman filtering

Abstract

A seam tracking method is presented based on the estimation of weld position during the gas tungsten arc welding process. Kalman filtering of the weld pool images from a visual sensor is applied to compute recursively the solution to the weld position equations which are established based on an estimation of the centroid position of the weld pool images. This centroid, the position of which corresponds with the weld position, is extracted as the measurement eigenvector. The evolution of the weld position data from the weld pool images can be described through an appropriate process model, so that the weld position can be detected by applying a Kalman filter. This allows adjustment of the welding torch position in real time, which may significantly reduce processing time and promote seam tracking accuracy. Simulations and actual welding experiments have demonstrated the effectiveness of the proposed algorithm in the presence of weld pool image noise and have demonstrated the robustness of weld position detection for seam tracking.     

利用开关卡尔曼滤波器的目标跟踪技术研究

为了使机器人视觉伺服控制系统的目标跟踪精度得到进一步提高,构建一种基于开关卡尔曼滤波器的视觉伺服控制系统。研究视觉伺服的目标跟踪原理,推导相关的数学模型,并分析跟踪误差产生的原因;针对图像采集和处理引入的延时问题,通过卡尔曼滤波估计得到目标运动的速度信息,以此作为前馈量输入视觉伺服控制器,补偿由于目标运动和延时造成的跟踪误差;为了解决卡尔曼滤波器由于目标运动的突然变化而降低估计性能的问题,引入运动监视器以在目标运动突然变化时发出开关信号并重置卡尔曼滤波器;最后对该算法进行实验与仿真。结果表明:基于卡尔曼滤波器的视觉伺服控制器能把跟踪误差控制在1 mm以内,而开关卡尔曼滤波器能有效地减少因目标运动状态突然变化而产生的跟踪误差。     

车用涡轮增压器轴向气动作用力分析

针对涡轮增压器高转速工况下止推轴承损坏的现象,以某涡轮增压器为研究对象,分析涡轮增压器轴向气动力随不同工况的变化。建立包含轮背间隙、密封环间隙的增压器压气机与涡轮三维模型,在ANSYS/ICEM中进行网格划分,采用ANSYS/CFX求解器对不同工况下增压器压气机、涡轮的流场进行数值仿真。计算结果表明:随着转速增加,涡轮增压器轴向力合力增大,并且与理论值偏差增大;同一转速下,密封环间隙对于轴向力合力影响较小,而且小密封环间隙气体泄漏量小;同一转速下,随着流量的增加,增压器轴向合力随之减小。