Nonlinear online process monitoring and fault diagnosis of condenser based on kernel PCA plus FDA

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

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 and future considerations are made.

Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation

Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.

On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.

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 T~2 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 SPEEWMA 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%.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

A Novel MDFA-MKECA Method With Application to Industrial Batch Process Monitoring

For the complex batch process with characteristics of unequal batch data length,a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis(MDFA-MKECA)in this paper.Combining the mechanistic knowledge,different mixed data features of each batch including statistical and thermodynamics entropy features,are extracted to finish data pre-processing.After that,MKECA is applied to reduce data dimensionality and finally establish a monitoring model.The proposed method is applied to a reheating furnace industry process,and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

Local component based principal component analysis model for multimode process monitoring

For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.

Batch process monitoring based on multilevel ICA-PCA

In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.

A Web-based machining process monitoring system for E-manufacturing implementation

Recently, with the rapid growth of information technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries who want to adopt E-manufacturing system for the construction of globalization, agility, and digitalization to cope with the rapid changing market requirements. In this research, a real-time Web-based machine tool and machining process monitoring system is developed as the first step for implementing E-manufacturing system. In this system, the current variations of the main spindle and feeding motors are measured using hall sensors. And the relationship between the cutting force and the spindle motor RMS (Root Mean Square) current at various spindle rotational speeds is obtained. Thermocouples are used to measure temperature variations of important heat sources of a machine tool. Also, a rule-based expert system is applied in order to decide the machining process and machine tool are in normal conditions. Finally, the effectiveness of the developed system is verified through a series of experiments.

A new process monitoring method based on noisy time structure independent component analysis

Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.

On-Line Batch Process Monitoring Using Multiway Kernel Partial Least Squares

An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.

Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

Multi-sensor measurement and data fusion technology for manufacturing process monitoring:a literature review

Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

Application of Kernel Independent Component Analysis for Multivariate Statistical Process Monitoring

In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from kernel principal component analysis （KPCA） proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 （the sum of the squared independent scores） and squared prediction error （SPE） charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.

Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA

A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis(ICA) is proposed.The kernel ICA method is a two-phase algorithm:whitened kernel principal component(KPCA) plus ICA.KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel.ICA seeks the projection directions in the KPCA whitened space,making the distribution of the projected data as non-gaussian as possible.The application to the fluid catalytic cracking unit(FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables.Its performance significantly outperforms monitoring method based on ICA or KPCA.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder

In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.