Integrating independent component analysis and local outlier factor for plant-wide process monitoring

We propose a novel process monitoring method integrating independent component analysis (ICA) and local outlier factor (LOF). LOF is a recently developed outlier detection technique which is a density-based outlierness calculation method. In the proposed monitoring scheme, ICA transformation is performed and the control limit of LOF value is obtained based on the normal operating condition (NOC) dataset. Then, at the monitoring phase, the LOF value of current observation is computed at each monitoring time, which determines whether the current process is a fault or not. The comparison experiments are conducted with existing ICA-based monitoring schemes on widely used benchmark processes, a simple multivariate process and the Tennessee Eastman process. The proposed scheme shows the improved accuracy over existing schemes. By adopting LOF, the monitoring statistic is computed regardless of data distribution. Therefore, the proposed scheme integrating ICA and LOF is more suitable for real industry where the monitoring variables are the mixture of Gaussian and non-Gaussian variables, whereas existing ICA-based schemes assume only non-Gaussian distribution.     

Non-Gaussian chemical process monitoring with adaptively weighted independent component analysis and its applications

Chemical process monitoring based on independent component analysis (ICA) is among the most widely used multivariate statistical process monitoring methods and has progressed very quickly in recent years. Generally, ICA methods initially employ several independent components (ICs) that are ordered according to certain criteria for process monitoring. However, fault information has no definite mapping relationship to a certain IC, and useful information might be submerged under the retained ICs. Thus, weighted independent component analysis (WICA) for fault detection and identification is proposed to process useful submerged information and reduce missed detection rates of I² statistics. The main idea of WICA is to initially build the conventional ICA model and then use the change rate of the I² statistic (RI²) to evaluate the importance of each IC. The important ICs tend to have higher RI²; thus, higher weighting values are then adaptively set for these ICs to highlight the useful fault information. Case studies on both simple simulated and Tennessee Eastman processes demonstrate the effectiveness of the WICA method. Monitoring results indicate that the performance of I² statistics improved significantly compared with principal component analysis and conventional ICA methods.     

Ensemble modified independent component analysis for enhanced non-Gaussian process monitoring

As a multivariate statistical tool, the modified independent component analysis (MICA) has drawn considerable attention within the non-Gaussian process monitoring circle since it can solve two main problems in the original ICA method. Despite the diversity in applications, the determination logic for non-quadratic functions involved in the iterative procedures of MICA algorithm has always been empirical. Given that the MICA is an unsupervised modeling method, a direct rational study that can conclusively demonstrate which non-quadratic function is optimal for the general purpose of fault detection is inaccessible. The selection of non-quadratic functions is still a challenge that has rarely been attempted. Recognition of this issue and motivated by the superiority of ensemble learning strategy, a novel ensemble MICA (EMICA) modeling approach is presented for enhancing non-Gaussian process monitoring performance. Instead of focusing on a single non-quadratic function, the proposed method combines multiple base MICA models derived from different non-quadratic functions into an ensemble one, and the Bayesian inference is employed as a decision fusion method to form a unique monitoring index for fault detection. The enhanced fault detectability of the EMICA method is also illustrated on two industrial processes.     

A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant

Probabilistic principal component analysis (PPCA) based approaches have been widely used in the field of process monitoring. However, the traditional PPCA approach is still limited to linear dimensionality reduction. Although the nonlinear projection model of PPCA can be obtained by Gaussian process mapping, the model still lacks robustness and is susceptible to process noise. Therefore, this paper proposes a new nonlinear process monitoring and fault diagnosis approach based on the Bayesian Gaussian latent variable model (Bay-GPLVM). Bay-GPLVM can obtain the posterior distribution rather than point estimation for latent variables, so the model is more robust. Two monitoring statistics corresponding to latent space and residual space are constructed for PM-FD purpose. Further, the cause of fault is analyzed by calculating the gradient value of the variable at the fault point. Compared with several PPCA-based monitoring approaches in theory and practical application, the Bay-GPLVM-based process monitoring approach can better deal with nonlinear processes and show high efficiency in process monitoring.     

Robust multi-scale principal components analysis with applications to process monitoring

Robust multi-scale principal component analysis (RMSPCA) improves multi-scale principal components analysis (MSPCA) techniques by incorporating the uncertainty of signal noise distributions and eliminating/down-weighting the effects of abnormal data in the training set. The novelty of the approach is to integrate MSPCA with the robustness to the typical normality assumption of noisy data. By using an M-estimator based on the generalized T distribution, RMSPCA adaptively transforms the data in the score space at each scale in order to eliminate/down-weight the effects of the outliers in the original data. The robust estimation of the covariance or correlation matrix at each scale is obtained by the proposed approach so that accurate MSPCA models can be obtained for process monitoring purposes. The performance of the proposed approach in process fault detection is illustrated and compared with that of the conventional MSPCA approach through a pilot-scale setting.     

Nonlinear process monitoring using kernel dictionary learning with application to aluminum electrolysis process

In practice, because of complex mechanism processes, such as heating process, volume heterogeneity, and various chemical reaction characteristics, there is a nonlinear relationship among variables in industrial systems. The nonlinearity brings some difficulties to process monitoring. In order to ensure that the process monitoring system can work normally in nonlinear production processes, the nonlinear relationship between variables ought to be considered. In this work, a new fault detection and isolation method based on kernel dictionary learning is presented. In detail, the linearly inseparable data is mapped to a high-dimensional space. Then, a new nonlinear dictionary learning method based on kernel method was proposed to learn the dictionary. After obtaining the dictionary, the control limit can be calculated from the training data according to the kernel density estimation (KDE) method. When new data arrive, they can be represented by the well-learned dictionary, and the kernel reconstruction error can be used as a classifier for process monitoring. As for the fault data, the iterative reconstruction based method is proposed for fault isolation. In order to evaluate the effectiveness of the proposed process monitoring method, some extensive experiments on a numerical simulation, the continuous stirred tank heater (CSTH) process, and a real industrial aluminum electrolysis process are conducted. The proposed method is compared with several state-of-the-art process monitoring methods and the experimental results show that the proposed method can provide satisfactory monitoring results, especially for some small faults, thus it is suitable for process monitoring of nonlinear industrial processes.     

Electrogastrogram extraction using independent component analysis with references

Electrogastrogram (EGG) is a noninvasive measurement of gastric myoelectrical activity cutaneously, which is usually covered by strong artifacts. In this paper, the independent component analysis (ICA) with references was applied to separate the gastric signal from noises. The nonlinear uncorrelatedness between the desired component and references was introduced as a constraint. The results show that the proposed method can extract the desired component corresponding to gastric slow waves directly, avoiding the ordering indeterminacy in ICA. Furthermore, the perturbations in EGG can be suppressed effectively. In summary, it can be a useful method for EGG analysis in research and clinical practice.     

A source adaptive independent component analysis algorithm through solving the estimating equation

In this paper, a source adaptive algorithm for linear instantaneous independent component analysis is proposed. This new algorithm is based on solving the estimating equation through Newton’s method where no learning rate is needed which makes the proposed algorithm very easy to use. To achieve the source adaptivity, the density functions as well as their first and second derivatives are estimated by modified kernel density method. Empirical comparisons with several popular ICA algorithms confirm the efficiency and accuracy of the proposed algorithm.     

基于ICA-PCA的化工流程仪表故障诊断

传统的多元统计过程控制(MSPC)的故障诊断方法要求观测变量数据服从高斯分布,然而实际化工流程中的仪表数据中难以满足这一要求。针对这一问题,提出在仪表数据中提取分离出非高斯信息和高斯信息,并分别利用独立元分析法和主元分析法建立不同的故障诊断模型。在检测到发生故障后,通过改进的贡献度算法定位出发生故障的仪表。通过对Tennessee Eastman(TE)过程数据进行仿真研究,验证了ICA-PCA故障诊断法在化工流程仪表不同故障诊断中的有效性。     

Integrating independent component analysis and support vector machine for multivariate process monitoring

This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA–PCA and PCA–SVM.     

Face representation using independent component analysis

This paper addresses the problem of face recognition using independent component analysis (ICA). More specifically, we are going to address two issues on face representation using ICA. First, as the independent components (ICs) are independent but not orthogonal, images outside a training set cannot be projected into these basis functions directly. In this paper, we propose a least-squares solution method using Householder Transformation to find a new representation. Second, we demonstrate that not all ICs are useful for recognition. Along this direction, we design and develop an IC selection algorithm to find a subset of ICs for recognition. Three public available databases, namely, MIT AI Laboratory, Yale University and Olivette Research Laboratory, are selected to evaluate the performance and the results are encouraging.     

Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis

Hidden Markov models (HMMs) perform parameter estimation based on the forward–backward (FB) procedure and the Baum–Welch (BW) algorithm. The two algorithms together may increase the computational complexity and the difficulty to understand the algorithm structure of HMMs clearly. In this study, an increasing mapping based hidden Markov model (IMHMM) is proposed. Between the observation sequence and possible state sequence an increasing mapping is established. The re-estimation formulas for the model parameters are derived straightforwardly based on these mappings instead of FB variables. The IMHMM has simpler algorithm structure and lower storage requirement than the HMM. Based on IMHMM, an expandable process monitoring and fault diagnosis framework for large-scale dynamical process is developed. To characterize the dynamic process, a novel index considering serial correlation is used to evaluate process state. The presented methodology is carried out in Tennessee Eastman process (TEP). The results show improvement over HMM in terms of memory complexity and training time of the model. Also, the power of IMHMM can be observed compared with principal component analysis (PCA) based methods.     

Process monitoring of iron-making process in a blast furnace with PCA-based methods

Incidents happening in the blast furnace will strongly affect the stability and smoothness of the iron-making process. Thus far, diagnosis of abnormalities in furnaces still mainly relies on the personal experiences of individual workers in many iron works. In this paper, principal component analysis (PCA)-based algorithms are developed to monitor the iron-making process and achieve early abnormality detection. Because the process exhibits a non-normal distribution and a time-varying nature in the measurement data, a static convex hull-based PCA algorithm (SCHPCA) which replaces the traditional T²-based abnormality detection logic with the convex hull-based abnormality detection logic, and its moving window version, called the moving window convex hull-based PCA algorithm (MWCHPCA) are proposed, respectively. These two algorithms are tested on the real process data to verify their effectiveness in the early abnormality detection of iron-making process.     

核熵成分分析算法及其在化工过程监控中的应用研究

针对化工过程监测数据复杂、非线性等特点,本文将一种新的降维算法一核熵成分分析算法应用到化工过程监控。与其他的多元统计分析方法相比,核熵成分分析算法可以保证数据降维过程中的信息损失最小从而建立更加可靠的统计模型,进而提高故障检测的检出率。与核主成分分析相似,核熵成分分析也是将数据映射到一个高维空间,在高维空间中进行主元分析,不同之处是KECA在选取主元时采用了信息保有量较大的主元,使得数据在降维后的信息损失量更少。本文使用某石化企业的润滑油重质过程的数据测试算法监控效果,核熵成分分析算法的故障检出率为98.2%,比核主成分分析算法(69.706%)要高。实验结果显示,核熵成分分析算法的化工过程监控效果优于核主成分分析算法。     

基于独立源分析的过程监测方法

多元统计过程控制要求观测数据服从正态分布,而实际的5-业过程数据大都不满足正态分布条件.独立源分析（ICA）近几年才发展起来的一种新的统计方法,可以克服对数据分布的依赖性．对此,以ICA算法为核心,引入一种新型的过程监测方法,应用ICA提取独立源,利用I^2图,Ic^2图和SPE图进行故障检测．最后以3水箱系统为例进行了实验研究,取得了很好的效果．     

Detecting and isolating multiple plant-wide oscillations via spectral independent component analysis

Disturbances that propagate throughout a plant can have an impact on product quality and running costs. There is thus a motivation for the automated detection of plant-wide disturbances and for the isolation of the sources. A new application of independent component analysis (ICA), multi-resolution spectral ICA, is proposed to detect and isolate the sources of multiple oscillations in a chemical process. Its key feature is that it extracts dominant spectrum-like independent components each of which has a narrow-band peak that captures the behaviour of one of the oscillation sources. Additionally, a significance index is presented that links the sources to specific plant measurements in order to facilitate the isolation of the sources of the oscillations. A case study is presented that demonstrates the ability of spectral ICA to detect and isolate multiple dominant oscillations in different frequency ranges in a large data set from an industrial chemical process.     

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.     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.     

Process pattern construction and multi-mode monitoring

A novel framework for process pattern construction and multi-mode monitoring is proposed. To identify process patterns, the framework utilizes a clustering method that consists of an ensemble moving window strategy along with an ensemble clustering solutions strategy. A new k-independent component analysis-principal component analysis (k-ICA-PCA) modeling method captures the relevant process patterns in corresponding clusters and facilitates the validation of ensemble solutions. Following pattern construction, the proposed framework offers an adjoined multi-ICA-PCA model for detection of faults under multiple operating modes. The Tennessee Eastman (TE) benchmark process is used as a case study to demonstrate the salient features of the method. Specifically, the proposed method is shown to have superior performance compared to the previously reported k-PCA models clustering approach.