基于主元分析的FPSO故障检测与诊断

应用基于主元分析的故障诊断方法对浮式油轮生产储油卸油系统(FPSO)进行故障检测与诊断研究.选取FPSO油气水分离系统的18个主要过程监控变量为研究对象,通过对系统历史数据进行预处理分析,建立主元模型;利用主元模型对仿真实时数据进行故障检测,应用SPE统计法和Hotelling统计法判断系统是否发生故障;使用贡献图法实现故障分离.研究结果表明:基于主元分析的故障诊断方法可以准确地对FPSO生产过程的早期故障进行检测和诊断;且对于系统的细小扰动,动态主元分析法的故障诊断能力优于主元分析法.     

基于多尺度动态核主元分析的化工过程故障检测

针对化工过程数据的多尺度性和非线性特性,提出一种多尺度动态核主元分析方法.使用小波变换分析数据的多尺度特性,借助核函数来解决非线性映射问题,同时解决了噪声和干扰造成的各变量数据具有时间序列动态性问题.在此基础上,提出一种基于矩阵相似度量的核函数参数选优方法.将上述方法应用于TE模型的故障检测过程中,仿真结果表明,该方法提高了过程性能监视和故障检测的准确性,优于线性主元分析法的检测效果.     

一种改进的动态核主元分析故障检测方法

针对复杂工业系统动态非线性故障检测过程精度低和计算量大的问题,提出了一种改进的动态核主元分析故障检测方法,该方法首先利用不可区分度剔除相关程度较小或者不相关变量,减少数据量,然后通过观测值扩展对筛选后的新数据构建增广矩阵,并对矩阵使用核主元分析提取变量数据的非线性空间相关特征,最后通过监测T2和SPE两种统计量诊断出系统发生故障及识别故障变量。仿真实验证明,该方法能对风力发电机故障进行有效监测和诊断,与KPCA方法相比,改进的动态核主元分析方法对微小故障更为敏感。     

一种改进的动态核主元分析故障检测方法

针对复杂工业系统动态非线性故障检测过程精度低和计算量大的问题,提出了一种改进的动态核主元分析故障检测方法，该方法首先利用不可区分度剔除相关程度较小或者不相关变量,减少数据量，然后通过观测值扩展对筛选后的新数据构建增广矩阵，并对矩阵使用核主元分析提取变量数据的非线性空间相关特征，最后通过监测T ²和SPE 两种统计量诊断出系统发生故障及识别故障变量。仿真实验证明，该方法能对风力发电机故障进行有效监测和诊断，与KPCA方法相比，改进的动态核主元分析方法对微小故障更为敏感。     

主元空间中的故障分离与识别方法

主元分析 (PCA)作为数据驱动的一种统计建模方法,在化工产品质量控制与故障诊断方面得到广泛研究和应用.在故障重构技术的基础上,研究了基于T²统计量的故障分离和识别问题,分别获得了主元空间中故障可分离和识别的理论条件.以双效蒸发过程为例,对该生产过程中的10种不同故障进行仿真监测分析,证实了所获理论结果的有效性.     

一种基于可预测元分析的故障诊断方法

将可预测元分析(Fore CA)引入到过程监控中,通过选取合适的可预测元并构造能够反映系统运行状况的统计量对在线数据进行统计监控,克服了主元分析(PCA)方法假设数据服从高斯分布且无法反映系统动态时序特性的缺陷,能很好地描述工业过程的动态特性并进行故障检测。TE模型上的仿真结果证明了Fore CA在工业过程监控中的可行性与有效性。     

主元空间中的故障重构方法研究

主元分析 (PCA)作为一种数据驱动的统计建模方法,在化工产品质量控制与故障诊断方面获得了广泛研究和应用.利用故障子空间的概念,研究了基于T²统计量的故障重构问题,获得了主元空间中的完全重构、部分重构,以及可重构性的条件.为进一步在主元空间中进行故障分离和识别提供了可能.通过对双效蒸发过程的仿真监测,对不同传感器的故障类型、幅值等重要信息进行重构和波形估计,证实了所获结果的有效性.     

基于特征空间自适应k近邻工业过程故障检测

针对工业生产过程故障检测模型不能及时更新的问题,提出了一种特征空间自适应k近邻(featurespace adaptive k-nearest neighbor,FS-AkNN)故障检测方法。首先利用主元分析对训练数据进行降维,构建特征空间,然后利用k最近邻方法建立故障检测模型。在过程监视过程中,提出了基于距离规则的自适应更新故障检测模型。通过一个数值例子和TE过程的仿真实验结果表明了该方法的有效性。     

MPCA在间歇反应过程故障诊断中的应用

将多方向主元分析(MPCA)理论应用到一个实际的PVC间歇反应过程的性能监测与故障诊断中。由于间歇反应的特点,数据具有多维性,应用传统的主元分析将使过程的统计建模与故障诊断难以实现。MPCA可将间歇过程的多维数据沿时间轨迹分割,使得多批次的数据可以在各时间序列轨迹上建立相应的PCA模型,从而完成对间歇过程的实时监视及故障诊断。     

乳酸提纯新工艺动态建模仿真和控制系统设计

基于非平衡级和拟均相假设,建立了乳酸提纯反应精馏新工艺实验装置的动态机理模型.通过改进的数值计算方法提高了模型的求解效率,设计并实现了包含物性估算系统的模型仿真平台,以促进新工艺的工业化应用.利用仿真平台对新工艺装置进行了动态特性分析,在此基础上设计了两种单端质量控制方案:直接物料平衡和间接物料平衡方案.在不同类型和幅度的过程扰动下,分析比较了两种控制方案的调节性能.结果表明直接物料平衡方案控制品质优于间接物料平衡方案,可在不同扰动情况下满足过程的产品质量和转化率的联合控制要求.非平衡级动态机理模型能够反映反应精馏过程的动态特性,分析发现反应精馏过程有着独特的过程特性,基于机理模型的仿真平台是分析反应精馏特性的有效工具.     

多变量统计过程监控:进展及其在化学工业的应用

Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks an     

独立元分析方法(ICA)及其在化工过程监控和故障诊断中的应用

引　言近年来 ,多元统计过程控制 (multivariatestatisti calprocesscontrol,MSPC)作为一种基于多元统计投影理论的过程性能监控和故障诊断技术受到了学术界和工业界的广泛重视 ,并在化工生产过程中得到了成功应用[1] .MSPC中 ,人们采用主元分析方法(PCA)从过程观测数据中提取统计无关主元 ,通过构造各种信息统计量对过程运行状况进行统计分析 ,判断过程运行是否偏离了正常的操作区域并诊断引起状态偏移的原因 ,其结论成立的前提是要求观测数据服从正态分布[2 ] .然而 ,实际的工业过程数据大都不满足正态分布条件 ,传统的PCA必然导致…     

A new multivariate statistical process monitoring method using principal component analysis

Principal component analysis (PCA) has been used successfully as a multivariate statistical process control (MSPC) tool for detecting faults in processes with highly correlated variables. In the present work, a novel statistical process monitoring method is proposed for further improvement of monitoring performance. It is termed ‘moving principal component analysis’ (MPCA) because PCA is applied on-line by moving the time-window. In MPCA, changes in the direction of each principal component or changes in the subspace spanned by several principal components are monitored. In other words, changes in the correlation structure of process variables, instead of changes in the scores of predefined principal components, are monitored by using MPCA. The monitoring performance of the proposed method and that of the conventional MSPC method are compared with application to simulated data obtained from a simple 2×2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of MPCA is considerably better than that of the conventional MSPC method and that dynamic monitoring is superior to static monitoring.     

工业流化床反应器结块监视的动态PCA方法

Dynamic principal component analysis (DPCA) is an extension of conventional principal component analysis (PCA) for dealing with multivariate dynamic data serially correlated in time. Based on the fact that the measured variables in relation to chunk monitoring of the industrial fluidized-bed reactor are highly cross-correlated and auto-correlated, this paper presents a practical strategy for chunk monitoring by adopting DPCA in order to overcome the shortcomings of the conventional method. After introducing the basic principle of DPCA, both how to determine the time lagged length of data matrix and how to calculate the nonparametric control limits when the dynamic data are not subject to the assumption of independently identically distribution (IID) were discussed. An appropriate DPCA model based on the real data from a industrial fluidized-bed reactor was built, with parallel analysis and empirical reference distribution (ERD) method to select time lagged length and control limits, respectively. During data pretreatment, data smoothing was used to reduce noise and the serial correlations to some degree. The simulation test results showed the effectiveness of the DPCA based method.     

Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method

The development of simple and efficient monitoring methods for flooding supervision is an important but difficult task for the safe operation of packed towers. A data‐driven online flooding monitoring method named Bayesian integrated dynamic principal component analysis (IDPCA) is assessed. In the first step of IDPCA, using the fuzzy c‐means clustering method, the multivariate samples collected during plant operation are first classified into several groups. Then, in each subset a dynamic principal component analysis (DPCA) model is constructed to extract the process characteristics. To improve the monitoring performance, Bayesian inference is utilized to combine these DPCA models in a suitable manner. Consequently, the control limits are formulated using the probabilistic analysis. The superiority of IDPCA is illustrated using a lab‐scale packed tower by comparison with the conventional principal component analysis (PCA) and DPCA methods.     

Statistical monitoring of dynamic processes based on dynamic independent component analysis

Most multivariate statistical monitoring methods based on principal component analysis (PCA) assume implicitly that the observations at one time are statistically independent of observations at past time and the latent variables follow a Gaussian distribution. However, in real chemical and biological processes, these assumptions are invalid because of their dynamic and nonlinear characteristics. Therefore, monitoring charts based on conventional PCA tend to show many false alarms and bad detectability. In this paper, a new statistical process monitoring method using dynamic independent component analysis (DICA) is proposed to overcome these disadvantages. ICA is a recently developed technique for revealing hidden factors that underlies sets of measurements followed on a non-Gaussian distribution. Its goal is to decompose a set of multivariate data into a base of statistically independent components without a loss of information. The proposed DICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. DICA can show more powerful monitoring performance in the case of a dynamic process since it can extract source signals which are independent of the auto- and cross-correlation of variables. It is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. The simulation results clearly show that the method effectively detects faults in a multivariate dynamic process.     

Two‐step principal component analysis for dynamic processes monitoring

In this study, a two‐step principal component analysis (TS‐PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross‐correlation structure and dynamic auto‐correlation structure in process data simultaneously, TS‐PCA handles them in two steps: it first identifies the dynamic structure by using the least squares algorithm, and then monitors the innovation component by using PCA. The innovation component is time uncorrelated and independent of the initial state of the process. As a result, TS‐PCA can monitor the process in both steady state and unsteady state, whereas all other reported dynamic approaches are limited to only processes in steady state. Even tested in steady state, TS‐PCA still can achieve better performance than the existing dynamic approaches.     

基于平稳性能不确定信息盲源信号提取的过程监控方法

针对工业过程中的信息不一定平稳,提出了一种基于平稳性能不确定信息盲源信号提取的过程监控方法,并利用该方法提取过程盲源信号,采用k-近邻法进行分类,从而实现对过程性能的监控.通过对简单AR(1)过程和双效蒸发过程的仿真研究表明,这种方法是可行的.为了与基于传统独立成分分析(ICA)和多元统计过程控制(MSPC)的过程监控方法相比较,还作了相应的对比研究.结果表明,该方法比基于传统ICA的过程监控方法具有更少的误报率和漏报率,而比基于MSPC的过程监控方法具有更少的误报率,从而说明了该方法不仅是可行的,而且是有效的.     

Dynamic multivariate statistical process control using partial least squares and canonical variate analysis

Multivariate statistical techniques have been shown to be useful tools for multivariate statistical process control (MSPC) and process modelling. However, these approaches have been mainly applied to static systems. In the present work, a well known system representation, the state space model, is developed to deal with dynamic situations. The states of the system are approximated using two multivariate statistical projection techniques, Partial Least Squares (PLS) and Canonical Variate Analysis (CVA). These two model representations are compared both in terms of their predictive ability and also their monitoring power using a simulation example. An application to an industrial fluidised bed reactor will be presented at the conference following company approval.     

Statistical process monitoring based on dissimilarity of process data

Multivariate statistical process control (MSPC) has been widely used for monitoring chemical processes with highly correlated variables. In this work, a novel statistical process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The monitoring performance of the proposed method, referred to as DISSIM, and that of the conventional MSPC method are compared with their applications to simulated data collected from a simple 2 × 2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of DISSIM, especially dynamic DISSIM, is considerably better than that of the conventional MSPC method when a time-window size is appropriately selected.