Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process

Verron, Sylvain; Tiplica, Téodor; Kobi, Abdessamad

doi:10.1109/icit.2006.372301

Cited by 10 publications

(16 citation statements)

References 27 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“… as a plant‐wide control problem. Several works have used TEP for assessing methods of fault detection and diagnosis.…”

Section: Introductionsupporting

confidence: 73%

“…Some fault detection approaches have been tested on the TEP . In this work, however, we are more concerned with fault diagnosis techniques and some of them have been tested on the TEP with the plant‐wide control structure recommended in Lyman and Georgakis …”

Section: Applications To the Tepmentioning

confidence: 99%

“…Following this principle, a new procedure has been presented in Ref. for industrial process diagnosis, where a BC known as TAN (tree augmented network) was built after the identification of important variables made by computing the mutual information between each process variable and the class variable. The performance of this method was evaluated on the data of TEP.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Classifiers Applied to the Tennessee Eastman Process

et al. 2013

View full text Add to dashboard Cite

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.

show abstract

“… as a plant‐wide control problem. Several works have used TEP for assessing methods of fault detection and diagnosis.…”

Section: Introductionsupporting

confidence: 73%

Section: Applications To the Tepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Classifiers Applied to the Tennessee Eastman Process

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Various supervised learning models such as support vector machines (SVM) [17], the fisher discriminant analysis (FDA) [18], artificial neural networks (ANN) [18], the Bayesian network classifier [19,20] and multi-scale PCA and ANFIS [21] have been applied for fault classification of industrial processes. Among the mentioned methods, the artificial neural network of multilayer perceptron (MLP) type has received considerable attention due to its simplicity and high efficiency as non-linear classifier.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Designing supervised local neural network classifiers based on EM clustering for fault diagnosis of Tennessee Eastman process

Rad

Yazdanpanah

2015

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

“…For each subspace representing a fault pattern, a special NN is trained for fault diagnosis. Besides, Bayesian networks [27,28] and signed directed graphs (SDG) [29] are also investigated in TEP fault diagnosis problem.…”

Section: Related Work For Tep Fault Diagnosismentioning

confidence: 99%

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique

Xiao

2011

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This article employs adaptive rank-order morphological filter to develop a pattern classification algorithm for fault diagnosis in benchmark chemical process: Tennessee Eastman process. Rank-order filtering possesses desirable properties of dealing with nonlinearities and preserving details in complex processes. Based on these benefits, the proposed algorithm achieves pattern matching through adopting one-dimensional adaptive rank-order morphological filter to process unrecognized signals under supervision of different standard signal patterns. The matching degree is characterized by the evaluation of error between standard signal and filter output signal. Initial parameter settings of the algorithm are subject to random choices and further tuned adaptively to make output approach standard signal as closely as possible. Data fusion technique is also utilized to combine diagnostic results from multiple sources. Different fault types in Tennessee Eastman process are studied to manifest the effectiveness and advantages of the proposed method. The results show that compared with many typical multivariate statistics based methods, the proposed algorithm performs better on the deterministic faults diagnosis.

show abstract

Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process

Cited by 10 publications

References 27 publications

Bayesian Classifiers Applied to the Tennessee Eastman Process

Bayesian Classifiers Applied to the Tennessee Eastman Process

Designing supervised local neural network classifiers based on EM clustering for fault diagnosis of Tennessee Eastman process

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique

Contact Info

Product

Resources

About