Double-layer ensemble monitoring of non-gaussian processes using modified independent component analysis

The modified independent component analysis (MICA) was proposed mainly to obtain a consistent solution that cannot be ensured in the original ICA algorithm and has been widely investigated in multivariate statistical process monitoring (MSPM). Within the MICA-based non-Gaussian process monitoring circle, there are two main problems, i.e., the selection of a proper non-quadratic function for measuring non-Gaussianity and the determination of dominant ICs for constructing latent subspace, have not been well attempted so far. Given that the MICA method as well as other MSPM approaches are usually implemented in an unsupervised manner, the two problems are always solved by some empirical criteria without respect to enhancing fault detectability. The current work aims to address the challenging issues involved in the MICA-based approach and propose a double-layer ensemble monitoring method based on MICA (abbreviated as DEMICA) for non-Gaussian processes. Instead of proposing an approach for selecting a proper non-quadratic function and determining the dominant ICs, the DEMICA method combines all possible base MICA models developed with different non-quadratic functions and different sets of dominant ICs into an ensemble, and a double-layer Bayesian inference is formulated as a decision fusion method to form a unique monitoring index for online fault detection. The effectiveness of the proposed approach is then validated on two systems, and the achieved results clearly demonstrate its superior proficiency.     

Relevant and independent multi-block approach for plant-wide process and quality-related monitoring based on KPCA and SVDD

Due to prior knowledge being often unavailable in practice, a multi-block strategy totally based on data-driven analytics is an appropriate alternative for plant-wide processes. However, most recent multi-block methods are relatively vague or insufficient for dividing up the process space and lack the comprehensive fault information for quality-related monitoring. This work intends to develop a more reasonable multi-block method and demonstrate the negative impacts of quality-unrelated variables. Both motivations are entirely dependent on the correlation between variables. A major innovation is to determine those independent or related sets of variables, and to provide a more precise indication for those quality-related faults. Sub-blocks with related variables are each modeled by the KPCA, and the rest of the independent variables are treated as an input for a SVDD model. Finally, all of the statistical indicators are aggregated into a single statistic through Bayesian inference. The benefits of the proposed multi-block scheme (MKPCA-SVDD) are elaborated on in detail using numerical simulation, TE benchmark and industrial p-xylene oxidation process.     

基于改进RPCA的某型发射机自适应监测新方法

提出一种改进的基于数据块更新的递归主元分析(recursive principal component analysis,RPCA)方法,对具有慢时变和多变量等特性的某型舰空导弹武器雷达发射机工作过程进行自适应监测.该方法在协方差矩阵的特征值分解中引入低秩奇异值分解递归方法,实现负荷矩阵和特征值矩阵的递归计算;制定了均值、方差的更新策略;给出一种基于指数加权的控制限递归算法以提高RPCA的健壮性.实验证明该方法能自适应地跟踪过程时变并实时监测故障,同时有效地降低误警率.     

基于多变量统计过程监控的盾构机故障诊断

由于盾构机安装有众多监测仪表,且依靠单变量过程监控和人工诊断方式已经无法满足监测的要求,因此引入多变量统计过程监控方法(MSPM).主元分析(PCA)是应用最广泛的MSPM技术,PCA根据盾构运行监测的过程变量和历史数据建立数学模型,并计算统计监控量T2和平方预测误差δ,以及主元空间和残差空间的控制限,分析过程变量是否发生异常.最后以盾构刀盘驱动系统和螺旋输送液压系统为例说明MSPM的详细应用.     

基于WTPCA-MSVMs过程监控方法

提出了基于小波变换主元分析和多支持向量机(wavelet transform PCA-Multiple support vector machines,WTPCA-MSVMs)的过程监控方法,该方法首先利用小波变换(wavelet transform,WT)对采样数据进行预处理,以有效抑制过程数据中所含的噪声和干扰信号;然后利用主元分析(principal component analysis,PCA)对预处理后的数据建立主元监控模型;考虑到实际工业过程故障数据的数量较少,而支持向量机(support vector Machine,SVM)在小样本学习方面具有良好的泛化能力的特性,最后提出了基于多支持向量机(multiple support vector machines,MSVMs)的故障诊断方法。对TE(tennessee eastman,TE)过程的监控应用表明了所提出方法的有效性。     

Markov过程的柴油机监控系统可靠性评估

为评估柴油机监控系统的可靠性,提出了基于马尔科夫过程的柴油机监控系统可靠性评估方法.总结出了系统的功能结构框图,通过引入Markov过程描述系统状态从而建立了系统可靠性模型.通过不同方式获得了部件故障率参数.利用Matlab数值计算分析了部件故障率和维修率变化对系统可靠度及可用度的影响,得出了对系统可靠性有较大影响的部...     

Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis

Multivariate statistical methods have been widely applied to develop data-based process monitoring models. Recently, a multi-manifold projections (MMP) algorithm was proposed for modeling and monitoring chemical industrial processes, the MMP is an effective tool for preserving the global and local geometric structure of the original data space in the reduced feature subspace, but it does not provide orthogonal basis functions for data reconstruction. Recognition of this issue, an improved version of MMP algorithm named orthogonal MMP (OMMP) is formulated. Based on the OMMP model, a further processing step and a different monitoring index are proposed to model and monitor the variation in the residual subspace. Additionally, a novel variable contribution analysis is presented for fault diagnosis by integrating the nearest in-control neighbor calculation and reconstruction-based contribution analysis. The validity and superiority of the proposed fault detection and diagnosis strategy are then validated through case studies on the Tennessee Eastman benchmark process.     

一种新的多阶段间歇过程在线监控策略

为克服多阶段间歇过程硬划分和误分类导致漏报率和误报率高的缺陷,同时也为了实现更精确、有效地过程监控,提出一种基于模糊聚类软过渡的多PCA监控策略,实现多阶段间歇过程的在线监控.首先计算每个时刻数据矩阵的相似度指标作为聚类输入,采用模糊聚类算法实现阶段划分,根据隶属度辨识相邻阶段间的过渡过程,之后建立一系列具有时变协方差的加权PCA模型,该方法能客观地揭示各阶段及过渡过程的特征多样性,较好地解决存在过渡过程的多阶段监控问题.最后通过将所提出的方法应用于工业青霉素发酵过程的监控中,验证了该方法的可行性和有效性.     

石化企业生产过程监控与管理系统关键技术研究

为满足石化企业生产过程监控与管理的需求,研制了基于Web的生产过程监控与管理系统。对系统开发中所涉及的关键技术进行了深入研究,提出了一个较为通用的基于Web的三层体系结构,借助Infoplus．21实时数据库和Oracle关系数据库,实现了实时数据采集与数据处理;综合应用可扩展标记语言和Office网络组件实现了系统的信息集成和复杂报表的编制;采用基于组件的编程技术和基于案例推理的生产调度策略,增强了系统的灵活性;系统采用Java、Java服务器网页、Servlet、JavaBean、可扩展标记语言和Office网络组件开发,表明了系统的实用性和可扩展性。     

基于集成熵KPCA的复杂机电系统状态监测方法

针对传统KPCA方法的模型参数选取对经验知识依赖程度过高、容易造成漏报和误报的缺点,提出一种基于集成熵核主成分分析的状态监测方法。该方法将传统的KPCA与信息熵结合,在高维空间用信息测度确定模型参数,用Renyi熵贡献提取核主成分,通过构造综合统计量进行状态监测。在TE过程和某企业的压缩机组系统上的仿真研究表明,所提方法较传统KPCA有更好的非线性数据处理能力和更高的故障或异常检测精度。     

基于Prodave的设备参数设定及监控系统

介绍了一种基于PLC的快速锻压机控制系统,该系统以S7-400 PLC为控制和数据处理核心,采用2个S7-300 PLC控制左操作机和右操作机,2个工控机分别实现参数设定和过程监控。系统采用了工业以太网实现S7-400 PLC与工控机之间通信的方法,采用基于Prodave IE动态链接库调用方法实现数据通信。重点研究了基于VB的参数设定及监控系统的不同类型数据处理方法,解决了S7 PLC和工控机不同环境的数据类型转换,为大型机械设备控制系统的设计开发提供了参考。     

Image edge detection based tool condition monitoring with morphological component analysis

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions.     

Symmetrical component filter based on line condition monitoring instrumentation system for mine winder motor

As the capital investment in underground coal mining is huge enough by the standards of any conventional industry hence coal production process has to be very efficient to make commercially viable. In a situation of intensive and massive investment, the economics of production would primarily depend on machine utilization indicated by machine availability. Thus machine available time i.e. the time that a machine is available to do productive work, has to be maximized, for best returns on capital invested and utilization of manpower.In this present research work an online condition monitoring instrumentation system has been developed for condition monitoring of mine winder motor. The instrumentation system has been developed based on current monitoring technique. The symmetrical current component present in the unbalanced motor current is sensed with the help of current transformer, current to voltage converter, all pass filters and adders. Any electrical fault in mine winder motor will produce unbalancing in the motor circuit and will cause for the development of symmetrical current component. The type of electrical fault can be determined by sensing the symmetrical current component. One important advantage of this condition monitoring technique is that the instrument can be made hand held and the same hand held instrument may be used for the fault diagnosis of other motors also.A novel condition monitoring instrumentation system based on symmetrical component filter has been developed for on-line condition monitoring of mine winder motor. The instrumentation system would be able to diagnose various incipient faults of mine winder motor and will increase the safety as well as availability of mine winder.The result obtained from symmetrical current component filter based motor diagnostic technique has been verified with the result obtained by axial leakage flux based motor diagnostic technique for similar simulated motor fault condition to pinpoint the exact faulty of condition of the model mine winder motor.     

Power online monitoring system based on LabVIEW

In orther to solve the problem of the much production points and the large workload of control system needs to be connected in Handan Iron and steel plant, and the production process was complex, and the branch of workshop were not fully coordinated, the LabVIEW was taken as a platform to design a set of power plant on-line monitoring system, its functions included data acquisition, data storage and analysis, trend analysis, fault diagnosis and other functions. First the server of the system was designed, it was included three modules of data acquisition, data storage, data released, and then designed the client of the system, included the system setting module, signal display module, signal monitoring and analysis module. Under guarantee the efficiency of fault detection, the storage space was greatly reduced. The results show that the monitoring system was convenient early warning before malfunction immediately, and after failed, it was convenient for the engineering technical personnel to call history data, judged fault reason and analysis. [ABSTRACT FROM AUTHOR]     

一种基于光电探测技术的非接触式液位监测法

为了实现非接触式液位监测,现基于光电探测技术,针对不同液位状态下光线横穿透明容器后的出射光强进行分析,使用MatLab软件仿真并通过相关实验验证.结果表明:液位下降过程,输出光强会出现脉冲.通过监测该脉冲,间接实现非接触式液位监测,该方法不会造成溶液成分的改变,工作稳定,性能可靠,并能进行功能性扩展.     

基于WinCC的电镀过程监控系统

为了提高某电镀系统产品的合格率,采用西门子公司的WinCC和PLC对该系统进行自动化改造。该系统实现了数据的显示、查看和打印,工艺选择和工艺参数的设置以及系统报警功能。通过改造后的该电镀系统目前已投入生产,经相关产品验收员检验,证明产品合格率高,操作方便实用。     

基于ICA-PCA的连续退火机组温度系统的多模型监测方法

冷轧连续退火机组中,退火炉是其中的重要设备。为保证最终带钢的质量,带钢在退火炉内各个阶段的温度必须严格控制在工艺要求的范围之内。文章针对不同规格的带钢建立不同的温度监测模型,选用ICA-PCA两步结合的特征提取方法挖掘与温度状态紧密相关的过程信息,同时建立多个统计量指标及时实现带钢温度系统的在线监测。通过对现场实际数据的仿真分析证明了所提出方法的可行性和有效性。     

A repeated single-channel mechanical signal blind separation method based on morphological filtering and singular value decomposition

This paper proposes a repeated blind source separation (BSS) method based on morphological filtering and singular value decomposition (SVD) to separate the mixed sources from a single-channel signal. Firstly the signal is de-noised by the morphological filter and, the noise which affects the accuracy of the separation is removed. Next, the purified signal is reconstructed in phase space, and the SVD is applied to this matrix. After choosing the effective singular values, the inverse transform is applied to the revised signal matrix. From this, the pseudo signal can be obtained. The pseudo signal and the purified original signal are used to achieve the mixed sources separation through the fast independent component analysis (FastICA) algorithm. Then, the methods above are repeated in order to separate the weaker signals. The analysis of simulation and practical application demonstrates that that proposed method shows a high level of separating performance of a single-channel signal.     

基于统计模量分析间歇过程故障检测方法研究

针对间歇生产过程非线性、动态性、多阶段、采样不同步等特点,提出一种基于统计模量分析的故障检测方法.间歇过程统计模量反映原始数据特征,可以有效提取过程的非线性特征,保证了统计模量近似服从高斯分布,并使采样数据在不丢失信息的前提下同步化处理.在统计模量训练集中应用主元分析(PCA)方法进行故障检测,可以满足T2和SPE控制限确定的假设要求,使故障检测的准确性与可靠性得到提高.通过在盘尼西林发酵间歇过程中的应用和与多向主元分析(MPCA)、核主元分析(KPCA)等方法对比,验证了此方法的有效性.