基于主元子空间故障重构技术的故障诊断研究

针对基于主元分析(PCA)的统计性能监控法,由于不用过程机理模型的信息,因此,对故障诊断问题有难以在理论上作系统分析的缺陷,于是提出了一种基于主元子空间故障重构技术的故障诊断方法。利用故障子空间的概念,在故障重构技术的基础上,研究基于T~2统计量的故障诊断问题,提出故障识别指标和诊断算法。通过对双效蒸发过程的仿真监测,验证该诊断方法的有效性。     

Comprehensive subspace decomposition and isolation of principal reconstruction directions for online fault diagnosis

Reconstruction based fault diagnosis isolates the fault cause by finding fault subspace to bring the faulty data back to normal. However, the conventional reconstruction model was often defined using principal component analysis (PCA) to extract the general distribution information of fault data and may not well discriminate fault from normal status. It thus may fail to recover the fault-free data efficiently. To overcome the above problem, a relative principal component of fault reconstruction (RPCFR) modeling algorithm is proposed in the present work for fault subspace extraction and online fault diagnosis. Instead of directly modeling fault data to extract the reconstruction directions, the algorithm gives the original fault space a comprehensive decomposition according to its relationship with the normal process information. Those fault directions that can more efficiently characterize the effects of fault deviations relative to normal data are separated from the others and used for fault reconstruction. Its performance on online fault diagnosis is illustrated by the data from the Tennessee Eastman process.     

CSMWPCA方法及其在批过程故障诊断中的应用

鉴于传统的多向主元分析（MPCA）难以保证在线状态监测和故障诊断的实时性,提出了一种基于特征子空间的滑动窗主元分析（CSMWPCA）故障监测与诊断方法。在实时故障监测与诊断时,该方法采用适当大小的滑动窗逐步更新当前子数据空间,对当前子数据空间故障的识别通过依次计算其与基底库中各故障的匹配度来进行,克服了传统的MPCA不能处理非线性过程和实时性问题。与一种新的移动窗多向主元分析（MWMPCA）方法相比,CSMWPCA方法能更有效地识别故障发生的原因。     

Fault subspace decomposition and reconstruction theory based online fault prognosis

In this paper, a monitoring-statistic-based fault subspace decomposition and double decomposition (DD) reconstruction method is presented to deal with multivariable continuous slow-varying process fault prognosis. The new method assumed that fault is known and can be completely reconstructed. Then, the fault directions are determined by the monitoring-statistic-based fault subspace decomposition method. Finally, the DD reconstruction method and the vector autoregression (VAR) method are used to calculate and predict the magnitude degeneration process of corresponding fault directions. After that, online fault prognosis is realized. The experiments show the effectiveness of the developed method.     

基于特征子空间的滑动窗PCA在批过程故障诊断中的应用

基于传统的多向主元分析MPCA(multiway principal component analysis)常会导致误诊断,且对批过程难以保证在线状态监测和故障诊断的实时性,提出了一种基于特征子空间的滑动窗主元分析方法。在实时故障监测与诊断时,该方法采用适当大小的滑动窗逐步更新当前子数据空间,对当前子数据空间故障的识别通过依次计算其与基底库中各故障的匹配度来进行。这种方法克服了传统的MPCA不能处理非线性过程和实时性问题,并避免了MPCA在线应用时预报未来测量值带来的误差, 提高了批过程性能监测和故障诊断的准确性。     

基于广义主成分分析的重构故障子空间建模方法

在线故障诊断是工业过程中十分重要的问题.相比传统贡献图而言,基于重构的故障诊断受到特别关注.传统的主元分析方法没有考虑故障数据中同时包含正常工况信息和故障信息,因而提取出故障子空间对故障的描述准确性不足.为提高故障子空间的准确性,提出一种基于广义主成分分析的重构故障子空间建模方法.首先,同时考虑正常工况数据和故障数据,...     

Determining the number of principal components for best reconstruction

A well-defined variance of reconstruction error (VRE) is proposed to determine the number of principal components in a PCA model for best reconstruction. Unlike most other methods in the literature, this proposed VRE method has a guaranteed minimum over the number of PC's corresponding to the best reconstruction. Therefore, it avoids the arbitrariness of other methods with monotonic indices. The VRE can also be used to remove variables that are little correlated with others and cannot be reliably reconstructed from the correlation-based PCA model. The effectiveness of this method is demonstrated with a simulated process.     

A distribution-free method for process monitoring

Traditional multivariate statistical process control methods such as principal component analysis are limited to Gaussian process data when they used for process monitoring. However, the deficiency is not due to the method itself, but lies in the monitoring statistic construction and its confidence limit determination. This paper proposed a distribution-free method, which employs the one-class SVM to construct new monitoring statistics. Thus two new statistics are developed separately in two subspaces of the PCA model: the principal component subspace and the residual subspace. When some fault has been detected, a novel fault reconstruction scheme is proposed. For fault identification, two new identification indices are constructed. The performance of the proposed method in fault detection, reconstruction and identification is evaluated through a case study of the Tennessee Eastman (TE) benchmark process.     

基于故障重构的故障识别

针对基于主元分析(Principal Component Analysis,PCA)的统计过程性能监测法,尽管不依赖于精确的数学模型,然而却限制了其在故障诊断方面的能力问题,在故障重构技术的基础上,研究了基于统计量的故障诊断问题,获得了主元子空间中故障可重构性的理论条件,提出了故障识别指标和诊断算法.通过对双效蒸发过程...     

An improved PCA scheme for sensor FDI: Application to an air quality monitoring network

In this paper a sensor fault detection and isolation procedure based on principal component analysis (PCA) is proposed to monitor an air quality monitoring network. The PCA model of the network is optimal with respect to a reconstruction error criterion. The sensor fault detection is carried out in various residual subspaces using a new detection index. For our application, this index improves the performance compared to classical detection index SPE. The reconstruction approach allows, on one hand, to isolate the faulty sensors and, on the other hand, to estimate the fault amplitudes.     

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that existing work suffers from inherent limitations if complex fault scenarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.     

粗糙集和主元分析在基于神经网络的模拟电路故障诊断中的应用

针对基于神经网络的模拟电路故障诊断中,故障特征集维数过高带来的诊断难点,提出了利用粗糙集和主元分析法对故障特征集进行预处理。粗糙集对故障诊断决策表进行属性约简,主元分析进行数据压缩及特征提取。试验仿真表明,对预处理后的数据进行识别,简化了神经网络结构,可有效提高网络的训练速度与诊断效率。     

Variable reconstruction and sensor fault identification using canonical variate analysis

The detection and identification of faults in dynamic continuous processes has received considerable recent attention from researchers in academia and industry. In this paper, a canonical variate analysis (CVA)-based sensor fault detection and identification method via variable reconstruction is described. Several previous studies have shown that CVA-based monitoring techniques can effectively detect faults in dynamic processes. Here we define two monitoring indices in the state and noise spaces for fault detection and, for sensor fault identification, we propose three variable reconstruction algorithms based on the proposed monitoring indices. The variable reconstruction algorithms are based on the concepts of conditional mean replacement and object function minimization. The proposed approach is applied to a simulated continuous stirred tank reactor and the results are compared to those obtained using the traditional dynamic monitoring technique, dynamic principal component analysis (PCA). The results indicate that the proposed methodology is quite effective for monitoring dynamic processes in terms of sensor fault detection and identification.     

Dynamic process fault monitoring based on neural network and PCA

A newly developed method, NNPCA, integrates two data driven techniques, neural network (NN) and principal component analysis (PCA), for process monitoring. NN is used to summarize the operating process information into a nonlinear dynamic mathematical model. Chemical dynamic processes are so complex that they are presently ahead of theoretical methods from a fundamental physical standpoint. NN functions as the nonlinear dynamic operator to remove processes' nonlinear and dynamic characteristics. PCA is employed to generate simple monitoring charts based on the multivariable residuals derived from the difference between the process measurements and the neural network prediction. It can evaluate the current performance of the process. Examples from the recent monitoring practice in the industry and the large-scale system in the Tennessee Eastman process problem are presented to help the reader delve into the matter.     

Efficient faulty variable selection and parsimonious reconstruction modelling for fault isolation

Reconstruction-based fault isolation, which explores the underlying fault characteristics and uses them to isolate the cause of the fault, has attracted special attention. However, it does not explore how the specific process variables change and which ones are most significantly disturbed under the influences of abnormality; thus, it may not be helpful to understanding the specifics of the fault process. In the present work, an efficient faulty variable selection algorithm is proposed that can detect the significant faulty variables that cover the most common fault effects and thus significantly contribute to fault monitoring. They are distinguished from the general variables that are deemed to follow normal rules and thus are uninformative to reveal fault effects. To further reveal the fault characteristics, the selected significant faulty variables are then chosen to obtain a parsimonious reconstruction model for fault isolation in which relative analysis is performed on these selected faulty variables to explore the relative changes from normal to fault condition. The faulty variable selection can not only focus more on the responsible variables but also exclude the influences of uninformative variables and thus probe more effectively into fault effects. It can also help in finding a more interesting and reliable model representation and better identify the underlying fault information. Its feasibility is illustrated with simulated faults using data from the Tennessee Eastman (TE) benchmark process.     

综述人脸识别中的子空间方法

如何描述每个个体人脸的特征,使之区别于其他个体,是人脸识别研究中的关键问题 之一.近年来提出了大量的方法,其中随着主元分析在人脸识别中的成功应用之后,子空间分析 因其具有描述性强、计算代价小、易实现及可分性好的特点,受到了广泛的关注.文中结合近年 来已发表的文献,按照线性和非线性的划分,对子空间分析在人脸识别中的应用作一回顾、比较 和总结,以供其他人参考.     

Statistical process monitoring using improved PCA with optimized sensor locations

The emphasis of most PCA process monitoring approaches is mainly on procedures to perform fault detection and diagnosis given a set of sensors. Little attention is paid to the actual sensor locations to efficiently perform these tasks. In this paper, graph-based techniques are used to optimize sensor locations to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. Meanwhile, an improved PCA that uses two new statistics of PVR and CVR to replace the Q index in conventional PCA is introduced. The improved PCA can efficiently detect weak process changes, and give an insight to the root cause about the process malfunction. Simulation results of a CSTR process show that the improved PCA with optimized sensor locations is superior to conventional methods in fault resolution and sensibility.     

时滞多变量系统PCA优化建模

主元分析（PCA）在工业生产过程的产品质量控制与故障诊断等方面已得到广泛应用,然而当过程的变量间存在着未知时滞性时,必须确定数据间的对应关系,否则PCA模型将会不准．基于此,提出了PCA优化建模方法．该方法以过程变量间的时滞常数为优化变量,在分析PCA模型特点基础上,确定主成分个数和SPE统计量为综合目标函数,并建立模型约束条件,采用遗传算法求解．最后给出了仿真实例,证明了所提出方法的有效性．     

Sensor fault detection, isolation and reconstruction using nonlinear principal component analysis

State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis (PCA) model. An extension of this approach based on a Nonlinear PCA (NLPCA) model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.     

基于主元分析得分重构差分的故障检测策略

基于主元分析(PCA)的统计过程控制方法通常假设数据的生成过程是独立同分布的.当数据存在多模态结构或过程变量非线性相关时, PCA方法的故障检测性能将受到影响.针对上述问题,本文提出一种基于PCA得分重构差分的故障检测策略.首先,应用PCA将输入空间分解为主元子空间和残差子空间;接下来,应用k近邻(k NN)规则重构当前样本得分向量并计算样本的得分重构差分向量;最后,计算得分重构差分向量的统计值并进行故障检测.本文方法不仅可以降低数据多模态和变量非线性相关等特征对过程故障检测的影响,同时可以降低统计量的自相关性、提高过程故障检测率.将本文方法在两个模拟例子和田纳西–伊斯曼(TE)过程中进行测试,并与PCA、核主元分析(KPCA)、动态主元分析(DPCA)和k最近邻故障检测(FD–k NN)方法进行对比分析,测试结果证明了本文方法的有效性.