Variable moving windows based non‐Gaussian dissimilarity analysis technique for batch processes fault detection and diagnosis

Li, Yuan; Zhang, Xinmin

doi:10.1002/cjce.22162

Cited by 12 publications

(7 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first simulated system implements a mathematical model from a fed-batch fermentation process for the production of penicillin (PENSIM, version 2.0, developed at the Illinois Institute of Technology and available at ). The PENSIM simulator is a well-known testing system used in several batch process monitoring studies. ,− It generates profiles exhibiting several realistic features, such as nonstationarity, nonlinearity, multistage behavior, noise, and natural process variation. Furthermore, it allows for full control of the operations conditions, normal or abnormal, and has the capability for simulating several types of faults, with different magnitude, at specific times, which enables the computation of accurate figures of merit for the monitoring approaches under analysis.…”

Section: Application Of the Cam Framework To A Large Scale Comparison...mentioning

confidence: 99%

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength

Rato

Rendall

Gomes

et al. 2016

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

A significant number of batch process monitoring methods have been proposed since the first groundbreaking approaches were published in the literature, two decades ago. The proper assessment of all the alternatives currently available requires a rigorous and robust assessment framework, in order to assist practitioners in their difficult task of selecting the most adequate approach for the particular situations they face and in the definition of all the optional aspects required, such as the type of preprocessing, infilling, alignment, etc. However, comparison methods currently adopted present several limitations and even some flaws that make the variety of studies available not easily generalizable or, in extreme situations, fundamentally wrong. Without a proper comparison tool decisions are made on a subjective basis and therefore are prone to be at least suboptimal. In this article we present a structured review of comparison methods and figures of merit adopted to assess batch process monitoring approaches, analyzing their strengths and limitations, as well as some common missuses. Furthermore, we propose a comparison and assessment methodology (CAM) and figures of merit that should be adopted in order to circumvent some of the limitations of the current procedures. We begin by addressing, in this article, the analysis of the methods' "detection strength". Detection strength regards the ability to correctly detect abnormal situations without resulting in excessive false alarms. We describe in detail how the comparison framework is implemented and illustrate its application in two case studies, encompassing a rich variety of testing scenarios (99 000 batch runs) and monitoring methods (2-way, 3-way, and dynamic methods, amounting to a total of 60 different combinations of methods and their variants). From this analysis, it stands out that the CAM methodology is suitable for comparing different monitoring methods, even when the number of methods and variants is very large. It provides simple, informative, and statisticalbased metrics to assess a given method's performance and is able to quantify the added value of alternative approaches. When applied to the two case studies considered, 2-way methods (with batch-wise unfolding) combined with missing data (MD) infilling and dynamic time warping (DTW) were found to provide adequate solutions. If a more parsimonious model is required, dynamic methods such as autoregressive principal components analysis (ARPCA) provide good alternatives and Tucker3 with current deviations (CD) infilling also presents an interesting performance for some fault scenarios. In general, no method can claim absolute supremacy in all faulty scenarios.

show abstract

Section: Application Of the Cam Framework To A Large Scale Comparison...mentioning

confidence: 99%

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength

Rato

Rendall

Gomes

et al. 2016

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Moreover, due to the development of sensors, computer applications, and distributed control technology, extensive chemical process data can be collected and stored [9,10], so data-driven methods reveal many advantages [11,12]. Compared with the methods based on knowledge and analytical models, data-driven methods that are implemented only by analyzing and mining useful information for fault detection and diagnosis do not require precise mathematical modeling and process knowledge [13,14,15]. For example, principal component analysis (PCA) can extract the principal components (PCs) that effectively represent almost all information in the training data set, and the statistics Hotelling’s T2 and squared prediction error (SPE) are constructed for fault detection [16].…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods

Wang

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate “fault–symptom” table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

show abstract

“…With the gradually increased demand of product quality and production safety in modern industries, multivariate statistical process control based on data driven is widely applied . It uses the statistical regularity of process fluctuations to analyze and evaluate production processes …”

Section: Introductionmentioning

confidence: 99%

Fault detection method based on principal component difference associated with DPCA

Cheng

Guo

2018

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

Aiming at the fault detection in a dynamic process with nonlinear or multimodal features, a new fault detection strategy based on principal component difference associated with dynamic PCA (Diff-DPCA) is proposed in this paper.First, augment the training data set using the time lag, and then obtain an augmented training data set. Second, find the K-nearest neighbor set of each sample in the augmented training set and calculate the mean of the K nearest neighbor set. Third, calculate the loading and score matrices of the augmented training data set using PCA, and then calculate the scores of the proposed mean above using the loading matrix. Next, calculate the difference between the scores of a sample and its corresponding mean. Finally, 2 new statistics are built in difference subspace and residual subspace respectively. Principal component difference associated with dynamic PCA, which inherit the ability of DPCA monitoring a dynamic process, can improve the fault detection rate in the process with multimodal or nonlinear characteristics; meanwhile, the new statistics present low autocorrelation levels. The proposed method in this paper and other traditional methods are applied to detect faults in 2 cases and the Tennessee Eastman process, such as PCA, DPCA, and FD-KNN.The experimental results indicate that the proposed method outperforms the conventional PCA, DPCA, and FD-KNN.

show abstract

Variable moving windows based non‐Gaussian dissimilarity analysis technique for batch processes fault detection and diagnosis

Cited by 12 publications

References 53 publications

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength

Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods

Fault detection method based on principal component difference associated with DPCA

Contact Info

Product

Resources

About