Fault detection using CUSUM based techniques with application to the Tennessee Eastman Process

Shams, Mohamed Bin; Budman, Hector; Duever, Thomas A.

doi:10.3182/20100705-3-be-2011.00019

Cited by 10 publications

(18 citation statements)

References 16 publications

(27 reference statements)

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“…Each of these methods has shown different levels of success in detecting and diagnosing the faults considered in the simulations (Table 1). Several statistical studies have reported faults 3, 9 and 15 as unobservable or difficult to diagnose due to the close similarity in the responses of the noisy measurements used to detect these faults [24,35,9,11] and therefore these 3 faults were not considered in the current study. 3 and 4 respectively.…”

Section: Case Study: Tennessee Eastman Processmentioning

confidence: 99%

Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

Agarwal¹,

Tamer²,

Budman³

2021

Preprint

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The focus of this work is on Statistical Process Control (SPC) of a manufacturing process based on available measurements. Two important applications of SPC in industrial settings are fault detection and diagnosis (FDD). In this work a deep learning (DL) based methodology is proposed for FDD. We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples. The explainability is quantified by a novel relevance measure of input variables that is calculated from a Layerwise Relevance Propagation (LRP) algorithm. It is shown that the relevances can be used to discard redundant input feature vectors/ variables iteratively thus resulting in reduced over-fitting of noisy data, increasing distinguishability between output classes and superior FDD test accuracy. The efficacy of the proposed method is demonstrated on the benchmark Tennessee Eastman Process.

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Section: Case Study: Tennessee Eastman Processmentioning

confidence: 99%

Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

Agarwal¹,

Tamer²,

Budman³

2021

Preprint

Self Cite

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“…Each of these methods has shown different levels of success in detecting and diagnosing the 20 faults considered in the simulations (Table 1). Several statistical studies have reported faults 3, 9 and 15 as unobservable or difficult to diagnose due to the close similarity in the responses of the noisy measurements used to detect these faults [31,43,10,15]. These 3 difficult to observe faults are referred hereafter as incipient faults.…”

Section: Review Of Fdd Techniques Relevant To the Tennessee Eastman P...mentioning

confidence: 99%

“…Another variant of the PCA approach that combines the results from the PCA algorithm with a Cumulative Sum (CUSUM) operation has shown to be a viable option to solve the detection problem of the incipient faults but a relative long time after occurrence of the fault is needed for detection. The reason is that the cumulative sum of PCA score values over a sufficient amount of time can provide detection of minor changes in the process variables that cannot be detected without using the CUSUM operation [43,44].…”

Section: Review Of Fdd Techniques Relevant To the Tennessee Eastman P...mentioning

confidence: 99%

“…Within the class of data-driven approaches several algorithms predominantly are based on multivariate statistical methods such as PCA (Principal Component Analysis) [57,54,31,43] or their dynamic variants such as DPCA (Dynamic Principal Component Analysis) [10,54,28,39,37] have been proposed. Since the above mentioned methods were based on assumptions of process linearity, nonlinear modelling techniques such as ANN (Artificial Neural Network) based methods are investigated to deal with nonlinear process behavior.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Deep Recurrent Neural Network based Method for Fault Detection and Diagnosis

Agarwal¹,

Gonzalez²,

Elkamel³

et al. 2020

Preprint

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A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.

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“…A recent study by the authors of the current paper has proposed the application of cumulative−sum (CUSUM) based models for the detection of faults in the Tennessee Eastman problem (TEP) . Bin Shams et al proposed the application of location CUSUM (LCS) and scale CUSUM (SCS) based models to detect three particular faults that have been found unobservable by other algorithms previously applied to the TEP. − ,− After demonstrating the detection capability of the univariate CUSUM-based methods for each one of the three faults, a Hotelling’s T 2 chart based on a cumulative sum of the observations was proposed for the individual or simultaneous detection of these three faults. The latter was found to be essential when large numbers of correlated variables are considered.…”

Section: Introductionmentioning

confidence: 95%

Enhancing Fault-Observability by the Use of Feedback Control

Shams¹,

Budman²,

Duever³

2011

Ind. Eng. Chem. Res.

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This paper deals with detection of faults in the Tennessee Eastman problems (TEP) that have been found unobservable by previous studies. Hotelling’s T 2 charting based on the cumulative sums of the faults’ relevant variables was successful in detecting these faults, however, with significant delays. To reduce these delays, it is proposed to retune the feedback controllers involving controlled and manipulated variables used for detection. An optimization-based methodology that searches for an optimal trade-off between fault detection and control performance is formulated. The resulting controller design is compared with a design previously reported in the literature showing that significant reductions in the detection delays and overall reductions in the plant’s costs can be achieved by proper tuning of controllers. Both individual and simultaneous occurrence of faults is considered. Although the proposed method was illustrated using TEP process, the approach is general and can be applied to other processes.

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Fault detection using CUSUM based techniques with application to the Tennessee Eastman Process

Cited by 10 publications

References 16 publications

Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

Hierarchical Deep Recurrent Neural Network based Method for Fault Detection and Diagnosis

Enhancing Fault-Observability by the Use of Feedback Control

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