磨机减速机滚动轴承特征提取和故障诊断研究

滚动轴承是水泥磨机减速机的核心组件,同时也是故障频发的部件之一,为保证其健康、安全、高效的运行,本文将独立分量分析(ICA)与支持向量机(SVM)方法结合,为磨机减速机滚动轴承的故障诊断提供一个新的思路。首先提取轴承不同故障状态下观测信号的独立分量,再对独立分量(ICA)进行奇异值分解从而得到特征信息,最后联合支持向量机(SVM)将特征信息进行故障识别。数据处理结果表明这种特征提取的方法是有效的。     

基于相关性小波奇异熵的滚动轴承故障特征提取

针对轴承故障信号受噪声影响严重,导致故障特征提取稳定性较差的问题,将小波变换、相关性、奇异值分解和信息熵理论相结合,提出一种基于相关性小波奇异熵的轴承故障特征提取方法。该方法首先将轴承信号进行小波分解,利用小波分解系数和噪声的相关性特点不同,引入相关计算以去除噪声的影响;然后对相关处理后的规范化系数进行奇异值分解,轴承的不同故障信息就体现在奇异值中;再利用信息熵的统计特性对奇异值进行不确定度计算;最后,以相关性小波奇异熵作为特征向量,通过概率神经网络对滚动轴承故障进行识别。实验表明:该方法能够有效地提取轴承故障特征,具有良好的容噪能力和稳定性。     

基于性能退化的单盘转子系统可靠性评估方法

由于碰摩及不平衡故障,转子系统会随着运行时间的增加,由刚开始的退化效果不明显,到进入偶然失效期有明显的退化现象,本文根据碰摩、不平衡的故障机理,以及滚动轴承,建立了系统动力学模型,然后运用龙格库塔进行数值仿真,依据仿真数据,得到转子系统的退化轨迹,最后基于退化数据进行可靠性的评估。     

基于EWT奇异值与支持向量机的往复机械冲击特征分析方法研究

以柴油发电机组传动系统为研究对象,通过经验小波变换(EWT)的方法获得其本征模式函数的奇异值,将该奇异值作为特征向量结合支持向量机(SVM)进行分类,从而实现柴油发电机组的故障识别。     

背压式汽轮机转子-汽封摩碰故障分析和诊断

广西石化公司某生产车间的汽轮机转子振值过高导致联锁停机,利用在线监测系统对故障原因进行了分析和诊断,准确判断出汽轮机转子汽封处存在摩碰故障。更换转子汽封后成功启机,保证了机组的稳定运行。     

基于CEEMD-SVD和ELM的滚动轴承故障诊断

针对滚动轴承故障特征难以提取和故障难以识别的问题,提出一种基于互补总体平均经验模态分解(CEEMD)与奇异值分解(SVD)和极限学习机(ELM)分类模型相结合的滚动轴承故障诊断方法。该方法首先通过CEEMD对原始采样信号进行分解,得到由高频到低频组成的固有模态分量(IMFS),通过互相关性分析筛选出最能表征原始信号的固有模态(IMF)分量,并对它进行奇异值分解,然后将分解得到的奇异值作为特征参数构造特征向量,最后采用ELM分析提取特征向量矩阵,并建立ELM分类模型,实现对轴承故障类型的识别。仿真结果表明该方法对滚动轴承的故障识别度达到了95.0%。     

基于复杂度特征的气液二相流流型识别方法

针对气液二相流压差波动信号的非平稳和非线性特征,提出了一种基于经验模式分解(EMD)复杂度特征和支持向量机的流型识别方法。该方法首先对二相流压差波动信号进行经验模式分解,将其分解为若干个固有模态函数(IMF),然后对每一个IMF分量提取复杂度特征作为流型特征向量,并以此作为输入参数建立支持向量机分类器来识别流型。对水平管内空气-水二相流的实验结果表明,文中提出的方法能准确地识别流型,从而为流型识别提供了一种新的有效方法。     

基于经验模式分解的气液二相流研究

为了更加准确地了解气液二相流动特性,文中用能量熵、奇异熵及Lyapunov指数结合经验模式分解(EMD)的方法对气液二相流进行研究.先对流型信号进行经验模式分解,然后由特征向量计算出经验模式能量熵、奇异熵及最大Lyapunov指数.计算结果表明:经过EMD分解的流型信号的2种熵值及Lyapunov指数能够体现信号的变化...     

转动设备振动监测方法及案例分析

为了准确诊断转动设备振动故障,解决振动问题,通过实际案例介绍了怎样选用振动监测方法和如何进行故障诊断、处理,总结了转动设备常见故障的振动分析方法。实践表明,不平衡、不对中、松动、流体扰动、动静碰摩、滚动轴承故障基本可以通过频谱、波形、相位及高频解调谱分析几种振动分析方法得到较准确的诊断。     

管道振动故障三维识别方法

针对传统的管道单通道故障诊断存在的局限性,提出将全信息小波包技术和支持向量机技术相结合的管道振动故障识别。用小波包变换对三通道信号分别分解,用能量法求出分解后各频带信号对应的能量,并构建三维全信息能量特征向量,作为支持向量机分类器的训练样本和测试样本。经验证,训练后的分类器能够对管道振动进行识别和诊断,并且提高了故障识别的效率和精度。     

Application of fault monitoring and diagnosis in process industry based on fourth order moment and singular value decomposition

Principal component analysis (PCA) and partial least squares (PLS) have been frequently used for process industry monitoring; however, their application on industrial sites is limited because they cannot be used to process data with non-Gaussian distribution. Independent component analysis (ICA) has become a powerful modelling method for non-Gaussian process monitoring. However, the ICA-based modelling method has been found to contribute to double the amount of data loss in feature extraction. There are two reasons for this. First, when the PCA algorithm is used to whiten the original data, the smaller principal component is discarded. Second, when selecting independent components, some smaller independent components will be discarded according to the evaluation index. The abovementioned two data feature extraction methods may discard useful information for fault monitoring, which will inevitably lead to inaccurate fault monitoring. To solve this problem, a fault monitoring and diagnosis method based on fourth order moment (FOM) analysis and singular value decomposition (SVD) is proposed. First, the fourth order moments of each process variable were constructed separately. Then, the data space of the fourth order moments was decomposed by singular value decomposition to establish the global monitoring statistics. Finally, the contribution diagram was drawn and the fault diagnosis was performed based on the global monitoring results. The proposed method was applied to the Tennessee Eastman (TE) simulation platform, and its effectiveness and feasibility were verified by a comparison with PCA and ICA.     

基于自适应量子蚁群算法的石脑油裂解炉故障诊断

针对实际生产过程中缺乏故障数据,采用适合小样本问题的支持向量机对石脑油裂解炉进行故障诊断。为了消除高维数据及系统噪声对故障诊断的干扰,将量子编码引入蚁群算法,提出一种新的自适应量子蚁群算法进行故障特征选择以进一步提高诊断性能。数值仿真实验结果显示,提出的自适应量子蚁群算法具有更好的全局寻优性能;对石脑油裂解炉传感器故障的诊断结果表明自适应量子蚁群算法能快速、准确地搜索到关键故障特征,有效地提高了支持向量机故障诊断的正确率和鲁棒性。     

基于ICA-SVM的复杂化工过程集成故障诊断方法

针对由于复杂操作或多回路控制等因素造成复杂化工过程故障诊断难度加剧问题,提出了一种基于独立成分分析（ICA）和支持向量机（SVM）的集成故障诊断方法。该方法利用快速ICA算法建立正常工况ICA模型,通过监控统计量I²、I_e²、SPE是否超过用核密度估计方法确定相应的置信限检测故障。如检测到故障发生,即用梯度算法计算每一个监控变量对统计量I²、I_e²、SPE的贡献度,根据观察贡献度变化情况初步诊断出可能的故障源,并利用支持向量机多分类算法诊断出初始故障源。利用丁二烯精馏装置的实际工业故障数据验证提出的ICA-SVM集成故障诊断方法的有效性。     

基于支持向量机MPLS的间歇过程故障诊断方法

1 INTRODUCTION In batch or fed-batch processes, raw materials are converted to products within a finite duration. In prac- tical production, the process commonly exhibits large variations from batch to batch due to such influencing factors as the quality fluctuation of raw materials, de- fect of equipments, contaminations, and other unpre- dicted disturbances. These variations may have an adverse effect on the final product quantity and quality. But it is generally difficult to discern th…     

基于自动特征工程的飞行器轴承故障诊断

针对飞行器轴承信号单一且噪声多、需要针对性特征以及需要高可解释性的问题,开发了涵盖具有自适应噪声的完全集合经验模态分解（CEEMDAN）、自动特征工程以及随机森林的故障诊断模型,模型核心为自动进行特征生成以及提取的特征工程。通过该特征工程能够根据不同对象的信号差异,自动提取出不同对象的有效特征,具备对象间的通用性,且该特征工程可根据样本量的不同调整有效特征的数量,丰富特征空间,具备灵活的可扩展性。验证表明,该涵盖自动特征工程的模型的故障分类准确率为95.32%,可较好地在大样本量下区分压缩机轴承上的不同故障。     

基于中心损失的条件生成式对抗网络的冷水机组故障诊断

针对冷水机组产生的故障数据不足,数据集中正常数据和故障数据数量不平衡,进而导致故障诊断精度下降的问题,提出一种基于中心损失的条件生成式对抗网络(central loss conditional generative adversarial network,CLCGAN)和支持向量机(support vector machine,SVM)的故障诊断方法。首先,CLCGAN利用少量真实故障数据生成新的故障数据;然后,将生成的故障数据与初始数据集混合,使正常数据与故障数据的数量达到平衡;最后,利用平衡数据集构建SVM模型进行故障诊断。在GAN生成冷水机组故障数据时,构建动态中心损失项并加入到目标函数中,利用动态的中心损失减少冷水机组生成的各种故障数据的类内距离,从而降低各个故障生成数据之间的重叠程度,增加生成数据的可靠性。在生成故障数据之前配置相应的故障标签,并输入到CLCGAN中指导数据生成过程,使生成的故障数据可以均衡地分布于各个故障类别。在ASHRAE 1043-RP数据集上对所提方法进行了验证,结果表明,相较于其他解决数据不平衡问题的故障诊断方法,所提方法具有更高的故障诊断准确率。     

基于PSO-SVM的催化裂化装置故障诊断研究

提出基于粒子群优化算法和支持向量机的催化裂化装置反应再生子系统故障诊断方法。利用粒子群优化算法的全局搜索特性,实现支持向量机的参数优化算法。根据支持向量机算法构建了催化裂化装置反应再生子系统故障诊断模型。结果显示,该诊断方法准确率高,具有较高的使用价值。     

AN INNOVATION STATE SPACE APPROACH FOR TIME SERIES FORECASTING

Abstract. An innovation state space modelling approach is presented in which the structures and parameters of a model are determined by an identification algorithm proposed by Tse and Weinert ( IEEE Trans. Automat. Contr. 120 (1975), 603–13) and the singular value decomposition technique. This approach is applied to two typical data series to illustrate its use, and its forecasting accuracy is compared with other time series approaches.     

Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM

In this paper, some drawbacks of original kernel independent component analysis (KICA) and support vector machine (SVM) algorithms are analyzed for the purpose of multivariate statistical process monitoring (MSPM). When the measured variables follow non-Gaussian distribution, KICA provides more meaningful knowledge by extracting higher-order statistics compared with PCA and kernel principal component analysis (KPCA). However, in real industrial processes, process variables are complex and are not absolutely Gaussian or non-Gaussian distributed. Any single technique is not sufficient to extract the hidden information. Hence, both KICA (non-Gaussion part) and KPCA (Gaussion part) are used for fault detection in this paper, which combine the advantages of KPCA and KICA to develop a nonlinear dynamic approach to detect fault online compared to other nonlinear approaches. Because SVM is available for classifying faults, it is used to diagnose fault in this paper.For above mentioned kernel methods, the calculation of eigenvectors and support vectors will be time consuming when the sample number becomes large. Hence, some dissimilar data are analyzed in the input and feature space.The proposed approach is applied to the fault detection and diagnosis in the Tennessee Eastman process. Application of the proposed approach indicates that proposed method effectively captures the nonlinear dynamics in the process variables.