Process Knowledge Discovery Using Sparse Principal Component Analysis

Gao, Huihui; Gajjar, Shriram; Külahçı, Murat; Zhu, Qunxiong; Palazoğlu, Ahmet

doi:10.1021/acs.iecr.6b03045

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…Research in this area is particularly dependent on the abundance of faulty process data to allow for proper training of the data analytics models to identify the right fault and the corresponding root cause, which can once again be greatly facilitated using the proposed GUI. Sparse chemometrics methods have been proposed 30–32 to enable the identification of the root cause but as in detection we expect more research to be performed using machine learning methods 22,33,34 . For the learning algorithms, the crucial issue will be the availability of abundant and realistic data to train and test these algorithms.…”

Section: Application Of Gui Outputmentioning

confidence: 99%

An easy to use GUI for simulating big data using Tennessee Eastman process

Andersen

Udugama

Gernaey

et al. 2021

Quality & Reliability Eng

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Data‐driven process monitoring and control techniques and their application to industrial chemical processes are gaining popularity due to the current focus on Industry 4.0, digitalization and the Internet of Things. However, for the development of such techniques, there are significant barriers that must be overcome in obtaining sufficiently large and reliable datasets. As a result, the use of real plant and process data in developing and testing data‐driven process monitoring and control tools can be difficult without investing significant efforts in acquiring, treating, and interpreting the data. Therefore, researchers need a tool that effortlessly generates large amounts of realistic and reliable process data without the requirement for additional data treatment or interpretation. In this work, we propose a data generation platform based on the Tennessee Eastman Process simulation benchmark. A graphical user interface (GUI) developed in MATLAB Simulink is presented that enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuous time‐dependent processes. An R‐Shiny app that interacts with the data generation tool is also presented for illustration purposes. The app can visualize the results generated by the Tennessee Eastman Process and can carry out a standard fault detection and diagnosis studies based on PCA. The data generator GUI is available free of charge for research purposes at https://github.com/dtuprodana/TEP.

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Section: Application Of Gui Outputmentioning

confidence: 99%

An easy to use GUI for simulating big data using Tennessee Eastman process

Andersen

Udugama

Gernaey

et al. 2021

Quality & Reliability Eng

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“…As opposed to early process measurements that were limited to key variables sampled at a few strategic locations, in modern processes, numerous process states and physical properties can be accessed and stored with high sampling frequencies with the aid of a vast network of sensor arrays. 3 A key step to analyzing such large data sets is effective complexity reduction, during which the data size is reduced and the fundamental information structure is preserved. Principal component analysis (PCA) is a popular method for achieving dimension reduction with applications in various scientific fields, 4 especially in the field of statistical process monitoring.…”

Section: ■ Introductionmentioning

confidence: 99%

“…In a related but rather different study, Gao et al 3 developed a high-level SPCA algorithm based on SPCA to obtain loading vectors with high captured variance and called it the forward SPCA. The approach was then applied to the Tennessee Eastman process to discover underlying correlations.…”

Section: ■ Introductionmentioning

confidence: 99%

“…In the final step, the correlation structure of the process based on the SPCA model is to be investigated. Analogous to the methodology followed in Gao et al, 3 as the variables loading onto the same PC are correlated, underlying connections can be deduced among these variables representing the physical system loading onto the same PCs as a result of conservation laws and the laws of thermodynamics. 25 Hence, the sparse PCs and the physical structure of the process are compared and interpreted.…”

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confidence: 99%

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Sparse PCA Support Exploration of Process Structures for Decentralized Fault Detection

Theisen

Palazoğlu

Dörgő

et al. 2021

Ind. Eng. Chem. Res.

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With the ever-increasing use of sensor technologies in industrial processes and more data becoming available to engineers, the fault detection and isolation activities in the context of process monitoring have gained significant momentum in recent years. A statistical procedure frequently used in this domain is principal component analysis (PCA), which can reduce the dimensionality of large data sets without compromising the information content. While most process monitoring methods offer satisfactory detection capabilities, understanding the root cause of malfunctions and providing the physical basis for their occurrence have been challenging. The relatively new sparse PCA techniques represent a further development of the PCA in which not only the data dimension is reduced but also the data are made more interpretable, revealing clearer correlation structures among variables. Hence, taking a step forward from classical fault detection methods, in this work, a decentralized monitoring approach is proposed based on a sparse algorithm. The resulting control charts reveal the correlation structures associated with the monitored process and facilitate a structural analysis of the occurred faults. The applicability of the proposed method is demonstrated using data generated from the simulation of the benchmark vinyl acetate process. It is shown that the sparse principal components, as a foundation to a decentralized multivariate monitoring framework, can provide physical insight toward the origins of process faults.

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“…The ability to diagnose a fault is greatly enhanced by improving the interpretability to the components with most contributions to the fault. To this end, Gajjar et al 21 showed that, as compared to PCA, SPCA accurately identified the faulty variables and aided the process of fault diagnosis.…”

Section: ■ Introductionmentioning

confidence: 99%

Least Squares Sparse Principal Component Analysis and Parallel Coordinates for Real-Time Process Monitoring

Gajjar

Külahçı

Palazoğlu

2020

Ind. Eng. Chem. Res.

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The unprecedented growth of machine-readable data throughout modern industrial systems has major repercussions for process monitoring activities. In contrast to model-based process monitoring that requires the physical and mathematical knowledge of the system in advance, the data-driven schemes provide an efficient alternative to extract and analyze process information directly from recorded process data. This paper introduces the least squares sparse principal component analysis to obtain readily interpretable sparse principal components. This is done in the context of parallel coordinates, which facilitate the visualization of high dimensional data. The key contribution is the establishment of control limits on independent sparse principal component and residual spaces to facilitate fault detection, complemented by the use of the Random Forests algorithm to carry out the fault diagnosis step. The proposed method is applied to the Tennessee Eastman process to highlight its merits.

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Process Knowledge Discovery Using Sparse Principal Component Analysis

Cited by 12 publications

References 23 publications

An easy to use GUI for simulating big data using Tennessee Eastman process

An easy to use GUI for simulating big data using Tennessee Eastman process

Sparse PCA Support Exploration of Process Structures for Decentralized Fault Detection

Least Squares Sparse Principal Component Analysis and Parallel Coordinates for Real-Time Process Monitoring

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