Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process

Eslamloueyan, Reza

doi:10.1016/j.asoc.2010.04.012

Cited by 106 publications

(51 citation statements)

References 27 publications

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“…Lau et al (2010) presented an adaptive neuro-fuzzy inference system for online fault diagnosis of a gasphase polypropylene production process. Eslamloueyan (2011) proposed a hierarchical artificial neural network for isolating the faults of the Tennessee-Eastman process, which was proved efficient.…”

Section: Steel Plate's Faults Datasetmentioning

confidence: 99%

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Steel Plates Faults Diagnosis with Data Mining Models

P.¹

2012

Journal of Computer Science

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Problem statement: Over the last two decades, Fault Diagnosis (FD) has a major importance to enhance the quality of manufacturing and to lessen the cost of product testing. Actually, quick and correct FD system helps to keep away from product quality problems and facilitates precautionary maintenance. FD may be considered as a pattern recognition problem. It has been gaining more and more attention to develop methods for improving the accuracy and efficiency of pattern recognition. Many computational tools and algorithms that have been recently developed could be used. Approach: This study evaluates the performances of three of the popular and effective data mining models to diagnose seven commonly occurring faults of the steel plate namely; Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps and Other_Faults. The models include C5.0 decision tree (C5.0 DT) with boosting, Multi Perception Neural Network (MLPNN) with pruning and Logistic Regression (LR) with step forward. The steel plates fault dataset investigated in this study is taken from the University of California at Irvine (UCI) machine learning repository. Results: Given a training set of such patterns, the individual model learned how to differentiate a new case in the domain. The diagnosis performances of the proposed models are presented using statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the C5.0 decision tree with boosting algorithm has achieved a remarkable performance with 97.25 and 98.09% accuracy on training and test subset. C5.0 has outperformed the other two models. Conclusion: Experimental results showed that data mining algorithms in general and decision trees in particular have the great impact of on the problem of steel plates fault diagnosis.

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Section: Steel Plate's Faults Datasetmentioning

confidence: 99%

“…Pruning is the last method used to increase the performance of the C5.0 DT model here. It consists of two steps; prepruning and post-pruning (Eslamloueyan, 2011). Prepruning step allows only nodes with minimum number of samples (node size).…”

Section: Classification Modelsmentioning

confidence: 99%

Steel Plates Faults Diagnosis with Data Mining Models

P.¹

2012

Journal of Computer Science

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“…Artificial neural networks (ANN) and support vector machine (SVM) are the most employed techniques applied to TEP fault diagnosis [22][23][24][25] among machine learning based methods. Eslamloueyan [26] further proposed hierarchical artificial neural network (HANN) to diagnosis faults for TEP. Fault pattern space is first divided to subspaces using fuzzy clustering algorithm.…”

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.

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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.

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“…Some articles add some clustering algorithms before ANN classification [29]. R.Eslamloueyan has proposed a duty-oriented hierarchical neural network (DOHANN) for isolating the faults of a relatively complex process.…”

Section: Introductionmentioning

confidence: 99%

A New Ensemble Fault Diagnosis Method Based on K-means Algorithm

Yang¹,

Liu²,

Zhang³

et al. 2012

IJIES

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A new ensemble algorithm based on K-means clustering and probabilistic neural network called K-means-PNN for classifying the industrial system faults is presented. The proposed technique consists of a preprocessing unit based on K-means clustering and probabilistic neural network. Given a set of data points, firstly the K-means algorithm is used to obtain K-temporary clusters, and then PNN is used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, K-means-PNN is applied to diagnose the faults in TE Process and compared with K-means clustering and back-propagation neural network called K-means-BP, algorithm. Simulation studies show that the proposed K-means-PNN algorithm compared with K-means-BP algorithm not only improves the accuracy in fault classification but also is a reliable and computationally efficient tool.

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Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process

Cited by 106 publications

References 27 publications

Steel Plates Faults Diagnosis with Data Mining Models

Steel Plates Faults Diagnosis with Data Mining Models

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique

A New Ensemble Fault Diagnosis Method Based on K-means Algorithm

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