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MSPCA-KECA的冷水机组故障监测及诊断

针对冷水机组同类型不同等级故障的变量变化存在差异会造成误诊断的问题，提出一种基于多尺度主元分析-核熵成分分析（MSPCA-KECA）的故障诊断策略。MSPCA提取故障特征，其输出作为KECA分类器的输入，实现故障的实时监测与自动诊断。首先，改进的MSPCA算法通过将小波多尺度分析与主元分析相结合，筛选故障信息可能存在的尺度直接重构并采用PCA提取故障特征，获取不同类型故障之间差异的同时也保留了同类型但不同等级故障之间的相似性，提高故障诊断的可靠性。之后建立KECA非线性分类器并引入一种新的监测统计量--散度测度统计量，使降维后不同特征信息之间呈现显著的角度差异，易于分类。最后，采用支持向量数据描述（SVDD）算法确定新统计量的控制限，以克服无法获知统计量分布的问题。通过对冷水机组数据的仿真研究，验证了MSPCA-KECA方法的可行性及有效性。     

基于MSPCA-KECA的冷水机组故障监测及诊断

针对冷水机组同类型不同等级故障的变量变化存在差异会造成误诊断的问题,提出一种基于多尺度主元分析-核熵成分分析(MSPCA-KECA)的故障诊断策略。MSPCA提取故障特征,其输出作为KECA分类器的输入,实现故障的实时监测与自动诊断。首先,改进的MSPCA算法通过将小波多尺度分析与主元分析相结合,筛选故障信息可能存在的尺度直接重构并采用PCA提取故障特征,获取不同类型故障之间差异的同时也保留了同类型但不同等级故障之间的相似性,提高故障诊断的可靠性。之后建立KECA非线性分类器并引入一种新的监测统计量——散度测度统计量,使降维后不同特征信息之间呈现显著的角度差异,易于分类。最后,采用支持向量数据描述(SVDD)算法确定新统计量的控制限,以克服无法获知统计量分布的问题。通过对冷水机组数据的仿真研究,验证了MSPCA-KECA方法的可行性及有效性。     

基于改进多尺度主元分析的丙烯聚合过程监测与故障诊断

针对工业过程故障检测问题,提出了一种改进多尺度主元分析方法。首先针对过程数据所具有的随机性、非平稳性及含有大量噪声等特点,提出了一种改进小波变换阈值去噪方法,移除原始过程数据中的大部分高频随机噪声,提高数据的置信度,然后应用小波多尺度分解将每个变量依次分解成逼近系数和多个尺度的细节系数,在各个尺度矩阵建立相应的主元分析模型,以模型统计量控制限为阈值,对小波系数重构得到综合尺度主元分析模型。将该改进多尺度主元分析方法应用于丙烯聚合过程监测与故障诊断研究中,研究结果表明,与传统多尺度主元分析相比,改进多尺度主元分析减少了误报率和漏报率,提高了过程监测与故障诊断的精度。     

化工过程混合故障诊断系统的应用

故障诊断是保障化工过程安全、平稳进行的一个重要工具。主成分分析法(PCA)作为典型的故障诊断方法,已经广泛应用于各类化工过程的故障诊断,但在复杂过程的故障类别判断上还存在不足。而人工免疫系统对于自我-非我的识别能力有助于对故障类别的判断,并且其良好的自适应、自学习能力,有助于在诊断过程中对系统的完善和改进。本文将主成分分析法与人工免疫系统结合,建立了一个新的混合故障诊断系统,实现对于化工过程故障的早期诊断,并用Honeywell公司的UniSim平台建立了一个动态的化工过程模型,对该诊断系统进行了验证。     

基于C#和Matlab的变压器故障诊断系统的开发

提出了一种基于C#和Matlab的变压器故障诊断系统的开发方案.以Visual Studio 2005为开发平台,C#为开发语言,调用Matlab相关程序,最终完成了故障诊断系统的开发.验证结果表明,该诊断系统诊断精度高、速度快.     

基于主元分析的延迟焦化过程连续故障检测策略

提出了一种新的主元分析在线故障检测策略,并以PSOG软件为平台,将其长期应用于某炼油厂延迟焦化过程的在线故障检测。结果表明了所提出故障检测策略的有效性,并从应用结果出发,提出了过程故障诊断应用于实际所需的进一步研究内容。     

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.     

一种基于改进MPCA的间歇过程监控与故障诊断方法

针对基于不同展开方式的多向主元分析(MPCA)方法在线应用时各自存在的缺陷,提出一种改进的基于变量展开的MPCA方法,实现间歇过程的在线监控与故障诊断。该方法采用随时间更新的主元协方差代替固定的主元协方差进行T²统计量的计算,充分考虑了主元得分向量的动态特性;同时引入主元显著相关变量残差统计量,避免SPE统计量的保守性,且该统计量能提供更详细的过程变化信息,对正常工况改变或过程故障引起的T²监控图变化有一定的识别能力;最后提出一种随时间变化的贡献图计算方法用于在线故障诊断。该方法和MPCA方法的监控性能在一个青霉素发酵仿真系统上进行了比较。仿真结果表明：该方法具有较好的监控性能,能及时检测出过程存在的故障,且具有一定的故障识别和诊断能力。     

基于主元分析-概率神经网络的制冷系统故障诊断

制冷系统由于内部物质形态的多样性以及系统参数间的高度耦合而较为复杂,也增加了出现故障后的检测及诊断难度。针对制冷系统常见的7种故障,包括局部故障与系统故障,运用主元分析法提取故障样本主要特征,对样本进行降维处理后,基于概率神经网络进行故障诊断。主元分析法可将原始的62个参数分解为相互独立的主元,根据累计贡献率选取一定量的主元,并将其样本输入概率神经网络进行故障诊断,结果表明结合主元分析后的概率神经网络在一定范围内对spread值不敏感,不仅诊断正确率有所提高,而且缩短了诊断耗时。可见,主元分析法的使用可有效优化概率神经网络的诊断性能。     

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety. Small or incipient irregularities may lead to severe degradation in complex chemical processes, and the conventional process monitoring techniques cannot detect these irregularities. In this study to improve the performance of monitoring, an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis (MSPCA) with cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts. The new Hotelling's T² and square prediction error (SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data. The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods. The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults. Moreover, a comparative study shows that the SPE-EWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.     

Improved multi-scale kernel principal component analysis and its application for fault detection

In this paper the multiscale kernel principal component analysis (MSKPCA) based on sliding median filter (SFM) is proposed for fault detection in nonlinear system with outliers. The MSKPCA based on SFM (SFM-MSKPCA) algorithm is first proposed and applied to process monitoring. The advantages of SFM-MSKPCA are: (1) the dynamical multiscale monitoring method is proposed which combining the Kronecker production, the wavelet decomposition technique, the sliding median filter technique and KPCA. The Kronecker production is first used to build the dynamical model; (2) there are more disturbances and noises in dynamical processes compared to static processes. The sliding median filter technique is used to remove the disturbances and noises; (3) SFM-MSKPCA gives nonlinear dynamic interpretation compared to MSPCA; (4) by decomposing the original data into multiple scales, SFM-MSKPCA analyze the dynamical data at different scales, reconstruct scales contained important information by IDWT, eliminate the effects of the noises in the original data compared to kernel principal component analysis (KPCA). To demonstrate the feasibility of the SFM-MSKPCA method, its process monitoring abilities are tested by simulation examples, and compared with the monitoring abilities of the KPCA and MSPCA method on the quantitative basis. The fault detection results and the comparison show the superiority of SFM-MSKPCA in fault detection.     

基于小波去噪与KPCA的TE过程故障检测研究

针对化工过程复杂非线性,并且含有噪声和随机干扰的特点,提出利用小波去噪与核主元分析(KPCA)相结合的方法来进行故障检测,既可以达到去噪、抗干扰的目的,又可以将输入空间中复杂的非线性问题转化为特征空间中的线性问题,从而解决了主元分析(PCA)方法在非线性过程中性能差的问题.并将该方法应用于Tennessee Eastm...     

化工过程危险剧情分类及SDG定性识别方法

系统深入地揭示危险“剧情”是分析和解决化工过程安全问题的基础和核心内容,也是安全评价和故障诊断的核心内容。本文对危险剧情进行了定义,并在将危险剧情分为5类的基础上,采用符号定向图（SDG）定性识别方法,提出了连续系统的智能化自解释报警、单故障和多故障根原因诊断的算法步骤。研究成果对过程系统的危险与可操作性分析（HAZOP）、安全防护层分析（LOPA）、自解释报警和故障诊断系统在过程工业领域的开发具有重要的理论与实际意义。     

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.     

空分设备粗氩塔氮塞的诊断与估计方法

为提高空分产品质量,降低氮塞故障造成的不利影响,应用互信息方法建立了空分系统的符号有向图模型,改进了基于有向图的故障诊断方法,提出了故障严重程度的判别方法,实现了空分系统粗氩塔氮塞故障的快速诊断以及氮塞严重程度的准确估计.     

GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

Principal component analysis (PCA) serves as the most fundamental technique in multivariate statistical process monitoring. However, other than determining contributions to a fault from each variable based on the pre-selected major principal components (PCs), the PCA-based fault diagnosis with an optimal selection of PCs is seldom investigated. This paper presents a novel Gaussian mixture model (GMM) and optimal principal components (OPCs)-based Bayesian method for efficient multimode fault diagnosis. First, the GMM and Bayesian inference is utilized to identify the operating mode, and then local PCA model is established in each mode. Second, given that the various principal components (PCs) may contain distinct fault signatures, the behavior of each PC in local PCA is examined and the OPCs are selected through stochastic optimization algorithm. Based on the OPCs, a Bayesian diagnosis system is then formulated to identify the fault statuses in a probability manner. Performance of GMM–OPC Bayesian diagnosis is examined through a numerical example and the Tennessee Eastman challenge process. The efficiency and feasibility are demonstrated.