A complete procedure for leak detection and diagnosis in a complex heat exchanger using data-driven fuzzy models

Habbi, Hacene; Kinnaert, Michel; Zelmat, M.

doi:10.1016/j.isatra.2009.01.004

Cited by 28 publications

(6 citation statements)

References 25 publications

(35 reference statements)

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“…An adaptive observer is used to estimate the overall heat transfer coefficient and detect a performance degradation of the heat exchanger [10]. Fuzzy models based on clustering techniques are used to detect leaks in a complex heat exchanger [11]. SVM and relevance vector machine (RVM) have been widely used in the condition monitoring and fault diagnosis of machinery [12].…”

Section: Related Research a Condition Monitoring Of Heat Exchangersmentioning

confidence: 99%

An Experimental Study of Clogging Fault Diagnosis in Heat Exchangers Based on Vibration Signals

et al. 2016

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The water-circulating heat exchangers employed in petrochemical industrials have attracted great attention in condition monitoring and fault diagnosis. In this paper, an approach based on vibration signals is proposed. By the proposed method, vibration signals are collected for different conditions through various high-precision wireless sensors mounted on the surface of the heat exchanger. Furthermore, by analyzing the characteristics of the vibration signals, a database of fault patterns is established, which therefore provides a scheme for conditional monitoring of the heat exchanger. An experimental platform is set up to evaluate the feasibility and effectiveness of the proposed approach, and support vector machine based on dimensionless parameters is developed for fault classification. The results have shown that the proposed method is efficient and has achieved a high accuracy for benchmarking vibration signals under both normal and faulty conditions. INDEX TERMS Vibration signal, heat exchanger, clogging fault, fault diagnosis, support vector machine.

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Section: Related Research a Condition Monitoring Of Heat Exchangersmentioning

confidence: 99%

An Experimental Study of Clogging Fault Diagnosis in Heat Exchangers Based on Vibration Signals

et al. 2016

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“…See the references for a sampling of applications [1][2][3][4][5][6][7][8]. Most of the control system vendors offer a FLC product.…”

Section: Product Sourcesmentioning

confidence: 99%

Fuzzy Logic Control in Processes and Automation

Rhinehart¹

2011

Instrument Engineers' Handbook

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“…So far, multivariate statistical techniques and machine learning have been widely used for fault detection and diagnosis of power plant equipment, such as principal component analysis (PCA) [4][5][6][7], independent component analysis (ICA) [8,9], auto-associative kernel regression (AAKR) [10,11], artificial neural networks [12,13], fuzzy models [14,15], support vector machine [15,16], neuro-fuzzy networks [17], and group method of data handling [18]. PCA and ICA can handle multivariate process data effectively via dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

Jang²,

Yoo³

et al. 2018

Energies

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Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space; the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods.

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A complete procedure for leak detection and diagnosis in a complex heat exchanger using data-driven fuzzy models

Cited by 28 publications

References 25 publications

An Experimental Study of Clogging Fault Diagnosis in Heat Exchangers Based on Vibration Signals

An Experimental Study of Clogging Fault Diagnosis in Heat Exchangers Based on Vibration Signals

Fuzzy Logic Control in Processes and Automation

A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

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