基于半监督学习和稀疏表示的质谱仪运行灵敏度故障检测算法

为了解决质谱仪运行灵敏度故障对环境监测和化学污染监测产生的影响,提高监测数据的准确性和可靠性,提出了一种基于半监督学习和稀疏表示研究质谱仪运行灵敏度故障检测算法。根据半监督学习的原理对质谱仪的运行数据进行划分,在混合数据中标记运行灵敏度的故障特征。通过对应特征处理原始数据,在去噪和归一化特征的基础上求取灵敏度系数。采用稀疏表示理论对系数转换,以约束范数关系建立故障监测目标函数,检测质谱仪运行灵敏度故障。结果表明：以3种类型运行灵敏度故障作为测试对象,新算法可以实现较高精度的故障检测,且不受样本数量的限制,具有一定的应用价值。     

基于稀疏过滤特征学习的化工过程故障检测方法

过程安全一直以来是化学工业中尤为重要的问题之一,故障检测与诊断（FDD）作为化工异常工况管理最有力的工具之一,给过程安全提供了保障。随着深度学习的发展,很多智能学习算法已经被提出,然而这些算法却很少被应用到FDD中来。提出了一种基于稀疏过滤和逻辑回归（SFLR）算法的化工过程故障检测新方法。采用TE过程和环己烷无催化氧化制环己酮过程对提出的方法进行了验证,结果表明,所提出的方法均具有较高的诊断精度,案例研究表明提出的方法可以及时有效地诊断出故障。     

基于稀疏性非负矩阵分解的故障监测方法

提出了基于稀疏性非负矩阵分解(SNMF)的故障监测方法。非负矩阵分解(NMF)是一种新的降维方法,可以得到原始数据的低秩近似矩阵。与传统的多元统计过程监控方法如主成分分析(PCA)相比,NMF对潜变量的性质没有假设,除了非负性的要求。将稀疏编码和非负矩阵分解方法结合在一起,因为施加了稀疏性的约束,稀疏性非负矩阵分解方法可以得到对数据更稀疏的表示。在分解时对低秩近似矩阵进行正交化处理,从而在降维时除去变量中的冗余信息,将信息集中到更少的投影方向上。然后,用SNMF方法来提取过程的潜变量,并定义新的监测指标来进行故障监测。使用核密度估计(KDE)方法来计算新定义的监测指标的控制上限。最后,将提出的基于SNMF的监测方法应用于TE过程来评估其监测性能,并与基于传统NMF和PCA的方法进行比较。仿真实验结果表明了所提出新方法的可行性。     

基于非线性动态全局局部保留投影算法的化工过程故障检测

传统基于核映射的非线性故障检测方法的性能受核函数类型和核参数的调优影响较大,且实际工业环境中对过程变量的非线性阶数存在很多物理限制。针对这一问题,提出一种非线性动态全局局部保留投影(nonlinear dynamic global-local preserving projections,NDGLPP)的故障检测算法。该方法首先使用动态全局局部保留投影算法对数据矩阵进行降维;然后对降维后的矩阵建立二阶多项式映射提取非线性空间的相关特性;接着通过迭代这两个步骤以获得高阶非线性映射;最后,将所提方法应用于乙烯精馏过程和Tennessee Eastman(TE)过程仿真中,验证了检测方法的有效性和可行性。     

基于互信息的分散式动态PCA故障检测方法

对现代大型复杂动态过程来讲,不同测量变量会存在不同的序列相关性,而且变量间的相互影响会体现在不同的采样时刻上。为此,结合利用分散式建模的优势,提出一种基于互信息的分散式动态过程故障检测方法。该方法在对每个测量变量都引入多个延时测量值后,利用互信息为每个变量区分出与其相关的测量值,并建立起相应的变量子块。这种变量分块方式使每个变量子块都能充分地获取与之相对应的自相关性与交叉相关性信息,较好地处理了数据的动态性问题。然后,利用主元分析（PCA）算法对每一变量子块进行统计建模从而建立起适于大规模动态过程的多模块化的故障检测模型。最后,通过实例验证该方法用于动态过程监测的可行性和有效性。     

基于马氏距离局部离群因子方法的复杂化工过程故障检测

为了满足实际的生产需要,复杂化工过程往往包含多个运行模态。同时过程的复杂性使得同一模态下的数据分布是一种高斯分布和非高斯分布混合存在的不确定情况。数据的多模态分布特性以及同一模态下数据分布的不确定性使得传统多元统计监控(MSPM)方法很难给出令人满意的结果。针对这一问题,本文提出一种新的马氏距离局部离群因子(MDLOF)方法进行故障检测。通过利用马氏距离挖掘变量局部结构中包含的有用信息,并对样本的邻域密度加以考虑,形成对数据分布具有鲁棒性的基于密度的监控指标。最后通过数值仿真例子及Tennessee Eastman过程验证其有效性。     

基于二阶差商LPP的多模态过程故障检测

为了提高局部保持投影(LPP)算法在各模态离散程度差异较大的多模态过程中故障检测性能,提出了一种新的基于二阶差商LPP(SODQ-LPP)的多模态过程故障检测方法。首先对多模态过程训练数据进行二阶差商预处理,消除模态间的方差差异,然后运用LPP算法进行降维和特征提取,计算样本的统计量,并利用核密度估计(KDE)确定控制限。对于新来的校验样本数据进行二阶差商处理后,向LPP模型上进行投影,计算新数据的统计量并与控制限比较进行故障检测。最后通过多模态数值例子和半导体过程数据的仿真实验结果验证了该算法的有效性。     

基于RISOMAP的非线性过程故障检测方法

化工过程监控数据存在非线性特点,且过程常常运行于多个模态,针对该类问题,提出基于相对等距离映射(relative isometric mapping, RISOMAP)的过程故障检测方法,该方法采用相对测地距离构造高维空间的距离关系阵,运用多维尺度变换(MDS)计算其低维嵌入输出,从高维数据中提取子流形信息和残差信息分别构造监控统计量进行故障检测,同时运用核ridge回归在线计算测试数据的低维输出,核矩阵通过综合相似度进行更新。数值算例和TE过程的仿真结果表明,RISOMAP方法可以更为有效地实施故障检测,故障检测的灵敏度较高,同时也为基于流形学习的多模态过程故障检测的实施提供了一条思路。     

基于KSFDA-SVDD的非线性过程故障检测方法

慢特征分析(SFA)是一种无监督的线性学习算法,没有考虑过程数据的类别信息和非线性特征。针对此问题,提出一种基于核慢特征判别分析(KSFDA)和支持向量数据描述(SVDD)的非线性过程故障检测方法KSFDA-SVDD。该方法首先利用核技巧将数据从原始空间映射到高维空间,然后通过最大化正常工况数据和故障模式数据之间伪时间序列的时间变化同时最小化正常工况数据内部伪时间序列的时间变化计算判别矩阵,最后利用SVDD描述采用判别矩阵降维后的正常工况数据的分布域,构建监控统计量检测过程故障。在连续搅拌反应器(CSTR)过程上的仿真结果表明所提出方法的故障检测性能优于传统的KPCA方法。     

基于VAE-OCCA的质量相关故障检测方法研究

由于闭环反馈系统的存在,并不是所有故障均会导致质量发生恶化。质量变量通常难以获得或具有一定的延迟,传统的无监督方法不能在检测过程是否正常的同时判断故障对质量的影响。典型相关分析(canonical correlation analysis,CCA)是一种经典的有监督方法,可以考虑输入输出间的关系,已被用于质量相关故障检测。然而,过程数据存在着维度高、非线性等问题,流程系统的复杂性使得CCA对于隐藏特征的捕获更具挑战性。提出了一种变分自编码器-正交典型相关分析(variational automatic encoder-orthogonal CCA,VAE-OCCA)方法。首先,利用变分自编码器对输入数据进行无监督自适应学习,实现对高维非线性过程变量的特征提取;进而,基于典型相关分析方法考虑输入输出关系,利用得到的相关系数矩阵进行奇异值分解建立质量相关和质量无关监测统计量;最后,通过工业案例测试说明提出方法的有效性及优越性。     

Multimode process monitoring strategy based on improved just-in-time-learning associated with locality preserving projections

In this paper, a multimode process monitoring strategy based on improved just-in-time-learning associated with locality preserving projections (IJITL-LPP) is proposed. First, raw data are projected into the feature space using locality preserving projections (LPP). Second, IJITL searches for similar samples of the query sample in the feature space by introducing a variational inference Gaussian mixture model (VIGMM). Finally, the new statistic named average distance is created to complete process monitoring. In the IJITL, the introduced VI can automatically determine the number of modes, thereby accelerating the efficiency of selecting similar samples. In the process monitoring phase, the average distance can reduce the impact of different mode dispersion on fault detection. In addition, LPP can render the model less sensitive to outliers. Compared with principal component analysis (PCA), LPP, K nearest neighbour rules, Gaussian mixture model (GMM), K-means based-PCA, and just-in-time-learning (JITL)-based LPP, the proposed method has better performance in a numerical case, the Tennessee Eastman process, and the semiconductor etching process.     

Fault detection for chemical process based on nonlinear dynamic global-local preserving projections

The performance of the traditional nonlinear fault detection method based on kernel mapping is greatly influenced by the type of kernel function and the tuning of kernel parameters. To solve this problem, a method named nonlinear dynamic global-local preserving projections(NDGLPP) is proposed for nonlinear process fault detection. Firstly, dynamic global-local preserving projection algorithm is used to reduce the dimension of data matrix. Since the second order polynomial mapping is established for the reduced dimension matrix to extract the relevant properties of nonlinear space. Then the two steps are iterated to obtain the higher-order nonlinear mapping. Finally, the proposed method is applied to the ethylene distillation process and Tennessee Eastman (TE) process simulation to verify the effectiveness and feasibility of the detection method.     

A fault detection method based on sparse dynamic canonical correlation analysis

Fault detection based on canonical correlation analysis (CCA) has received increased attention due to its efficiency in exploring the relationship between input and output. However, traditional CCA may generate redundant features in both the input and output projections while maximizing the correlations. In this paper, sparse dynamic canonical correlation analysis (SDCCA) is developed for dealing with the fault detection of dynamic processes. Through posing sparsity in the extraction of features, the interpretability of canonical variates is enhanced attributed to the sparsity of canonical weights. Based on the SDCCA model, the $$ monitoring metric is established for fault detection. Moreover, the upper control limit (UCL) based on $$ monitoring metrics is determined by the kernel density estimation (KDE) method to avoid the violation of the Gaussian assumption. The superiority of the proposed SDCCA-based fault detection method is illustrated through a comparative study of the Tennessee Eastman process benchmark.     

一种基于改进KICA的非高斯过程故障检测方法

针对基于核独立元分析(kernel independent component analysis,KICA)的故障检测方法只考虑非高斯信息提取而忽略局部近邻结构保持的问题,提出基于改进KICA的过程故障检测方法。将KICA法中只考虑非高斯信息提取的负熵最大化准则转换为熵最小化准则,结合局部保持投影的相似局部近邻结构准则,提出了同时考虑非高斯信息提取和局部近邻结构保持的目标函数,通过粒子群优化算法进行全局寻优,然后建立监控统计量对过程进行监控。在Tennessee Eastman过程上的仿真结果说明,与基于KICA的故障检测方法相比,所提方法能够在保持数据集局部近邻结构的同时,提取非高斯信息,能够有效缩短故障检测的延迟时间,提高故障检测率。     

Improved dynamic kernel PCA based on local preserving projections and its application for electric submersible pump fault diagnosis

Dynamic kernel principal component analysis (DKPCA) has been frequently implemented for nonlinear and dynamic process monitoring of complex industrial processes. However, traditional DKPCA focuses only on the global structural analysis of data sets and strongly neglects the local information, which is equally essential for process detection and identification. In this paper, an improved DKPCA, referred to as the local DKPCA (LDKPCA), is proposed based on local preserving projections (LPP) for nonlinear dynamic process fault diagnosis. The method combines the advantages of LPP and DKPCA by utilizing the local structure feature to maintain the geometric structure of the data in a unified framework. To achieve a highly comprehensive feature extraction, the local characteristics are fused in DKPCA to produce an optimization objective. The neighbouring points of the new objective function projection in the feature space are still maintained in proximity, and the variance information is retained simultaneously. For the purpose of fault detection, two statistics, known as the T² and squared prediction error (SPE) statistics, are constructed, based on the LDKPCA model, and used to monitor the latent variable space and the residual space, respectively. In addition, the sensitivity analysis is brought in for fault identification of the two statistics. Based on the experimental analysis using the shaft breakage data of an offshore oilfield electric submersible pump (ESP), the proposed method outperforms the conventional DKPCA in terms of fault monitoring performance. The experimental results demonstrate the potential of the method in nonlinear dynamic process fault diagnosis.     

基于LPP-GNMF算法的化工过程故障监测方法

提出了基于LPP-GNMF算法的化工过程故障监测方法。非负矩阵分解（NMF）是一种新兴的降维算法,由于它在机理上具有潜变量的正向纯加性的特点,所以在对数据进行压缩时,可以基于数据内部的局部特征有效描述数据信息,相比于传统的多元统计过程监控方法如主元分析（PCA）等有更好的解释能力。然而NMF要求原始数据满足非负性的要求,实际的化工过程有时并不能保证,为放宽对原始数据的非负要求,引入了广义非负矩阵分解（GNMF）算法。其次,GNMF在分解的过程中没有考虑到样本间的局部结构和几何性质,可能存在不能准确处理数据的问题。针对这一问题,提出了将GNMF与LPP（局部投影保留）相结合的算法。将提出的LPP-GNMF算法应用于TE过程来评估其监测性能,并与PCA算法、NMF算法、SNMF算法进行比较,仿真模拟结果表明所提算法的可行性。     

A novel fault classification method using reconstructed distance-based discriminant locality preserving projection for industrial processes

Fault detection and classification is a crucial issue in modern industrial processes for ensuring steady operation and high product quality. The process data collected and stored fully reflect the equipment running state and the production process. Moreover, the extracted nonlinear features can directly affect the effectiveness of the data-driven fault classification model. In this paper, a novel fault classification method based on nonlinear feature extraction using reconstructed distance-based discriminant locality preserving projection (RD-DLPP) is proposed. First, a hypersphere model for each class of data is developed according to the spatial structures and classes information in high-dimensional space. The hyperspheres are used as indicators to evaluate the discriminatory difficulty of samples. Second, the constraints of the correlations between the k-nearest neighbour points of the sample and the hypersphere are introduced, which can efficiently reconstruct new measure metrics between the sample and its k-nearest neighbour points. Finally, an improved fault classification model based on RD-DLPP is established for the construction of the highly discriminant subspace. The Bayesian decision is then used to classify the samples. The feasibility and efficiency of the proposed method are verified by the Tennessee Eastman process as a case study.