Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process

Kini, K Ramakrishna; Madakyaru, Muddu

doi:10.1109/access.2020.3037730

Cited by 13 publications

(5 citation statements)

References 42 publications

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“…This study compares PCA-T 2 , PCA-SPE, PCA-KD, KPCA-T 2 and KPCA-SPE strategies with the proposed KPCA-KD based strategy. The fault detection rate (FDR) and false alarm rate (FAR) metrics are used for a fair comparison between different strategies [41].…”

Section: Resultsmentioning

confidence: 99%

“…In fault detection problems, statistical indicator is compared with reference threshold to determine the presence of fault. In earlier studies on KD-based fault detection, a simple threesigma rule was used to compute the threshold for KD indicator [41]. The training data were initially split into two parts Tr1 and Tr2.…”

Section: A Kd Statistic Thresholdmentioning

confidence: 99%

“…The KD metric has been utilized for change point detection problem where it yielded good results with a minimum detection delay [40]. Later, the KD metric has been integrated with ICA modeling framework that enhanced monitoring results in comparison with the conventional indicators of PCA and ICA based strategies [41]. Though KD metric is focused on finding the distance between two distributions similar to KLD and HD based metrics, KD metric involves segment by segment comparison between the two distributions in a moving window of fixed length.…”

Section: Introductionmentioning

confidence: 99%

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Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

2022

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Fault detection is necessary for safe operation in modern process plants. The kernel principal component analysis (KPCA) technique has been widely utilized for monitoring non-linear processes because it enhances dimension reduction and fault detection in non-linear space. In this paper, an improved nonlinear fault detection strategy based on Kantorovich distance (KD) and kernel principal component analysis is proposed. The KD statistic is based on the optimal mass transport theory where the distance between two distributions is computed with respect to a cost function. The addressed fault detection problem models the data using the KPCA framework and utilizes the ability of the KD statistical indicator to detect faults. The detection stage involves comparing the residuals of training fault-free data and testing faulty data using the KD statistic. Additionally, the reference threshold for the KD statistic is computed using the kernel density estimation (KDE) approach as compared to the previously utilized three-sigma rule approach. The detection performance is illustrated with the help of three benchmark case studies: a continuous stirred tank reactor (CSTR) process, Tennessee Eastman (TE) process and an experimental distillation column process. The performance analysis suggests the superiority of the KPCA-KD fault detection scheme in monitoring various sensor faults. Moreover, comparison with traditional statistical indicators of PCA and KPCA schemes shows that the proposed scheme enhances fault detection and achieves an improved detection rate in monitoring different categories of faults.INDEX TERMS Kantorovich distance, kernal principal component analysis, continuous stirred tank reactor process, Tennessee Eastman process, experimental distillation column process, fault detection.

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Section: Resultsmentioning

confidence: 99%

Section: A Kd Statistic Thresholdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

2022

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“…In the MB-AMSAE network structure, the network structure corresponding to the stripper data block is 14-30-11-30-14, the network 4 and 5, which shows the false alarm rate and detection delay. Its monitoring effect is compared with that of PCA in the work of Kini and Madakyaru, [16] SAE, variable distribution characteristic and Bayesian inference (VDSPM) in the work of Huang and Yan, [32] AAE in the work of Jang et al, [26] and self-attention SAE (SA-SAE), and the appealed algorithms all incorporated a moving average with a window value of 4. The values in bold in Tables 4 and 5 indicate the best performance.…”

Section: Te Processmentioning

confidence: 99%

“…[15] A Kantorovich distance-based (KD) fault detection metric combined with a PCA strategy has been applied in process monitoring. [16] Ma et al focused on the internal characteristics of data, introduced attention mechanisms into PCA, and applied them to process monitoring. [17] With the increasing application of deep learning in process monitoring, numerous methods have been successfully applied in industrial processes.…”

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confidence: 99%

Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

Wang

Tang

et al. 2023

Can J Chem Eng

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The overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block‐based attention mechanism stacked autoencoder (MB‐AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self‐attention is used to focus on the information of each block itself. Cross‐attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee‐Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB‐AMSAE algorithm in process monitoring.

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A novel anomaly detection scheme for high dimensional systems using Kantorovich distance statistic

Madakyaru

Kini

2022

Int. j. inf. tecnol.

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The partial least squares (PLS) is a commonly applied multi-variate method in anomaly detection problems. The PLS strategy has been amalgamated with $$T^{2}$$ T 2 and squared prediction error (SPE) based statistical indicators to detect anomalies in process. These traditional indicators have few setbacks that has made them ineffective in monitoring applications. Hence, a statistical indicator based on Kantorovich distance (KD) is proposed for detecting sensor anomalies in this study. The proposed strategy integrates anomaly indicator based on KD metric with PLS based multi-variate method. The KD metric computes difference between the residuals of anomaly-free data and the data with anomaly and uses this distance as an indicator of anomaly. The proposed strategy’s critical feature is that a single anomaly indicator is sufficient to be integrated with PLS modeling framework. The Tennessee Eastman process benchmark and experimental distillation column processes data are used for assessing the performance of the proposed strategy. Further, comparisons have been provided between KD, $$T^{2}$$ T 2 , SPE and Generalized Likelihood Ratio indicators. The results demonstrate the superiority of the KD statistical indicator in detecting sensor anomalies in comparison to the traditional indicators of PLS based strategy. The KD indicator integrated with PLS framework also enhances the detection of small magnitude anomalies.

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Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process

Cited by 13 publications

References 42 publications

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

A novel anomaly detection scheme for high dimensional systems using Kantorovich distance statistic

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