Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks

Rostek, Kornel; Morytko, Łukasz; Jankowska, Anna

doi:10.1016/j.energy.2015.06.042

Cited by 69 publications

(28 citation statements)

References 33 publications

(39 reference statements)

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“…The artificial neural networks (ANNs), as universal approximators of any nonlinear input-output mappings, have been used in advanced control and fault detection schemes, both as process models and as nonlinear controllers [15]. The ANNs confirmed their ability to utilize real-time data taken from a running boiler system and periodically adapt to changeable process characteristics [16,17].…”

Section: Current Approaches To Fault Detection In a Pipeline System Omentioning

confidence: 99%

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Swiercz

Mroczkowska²

2020

Sensors

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In the paper the usability of the Multiway PCA (MPCA) method for early detection of leakages in the pipeline system of a steam boiler in a thermal-electrical power plant is presented. A long segment of measurements of selected process variables was divided into a series of “batches” (representing daily recordings of normal behavior of the plant) and used to create the MPCA model of a “healthy” system in a reduced space of three principal components (PC). The periodically updated MPCA model was used to establish the confidence ellipsoid for the “healthy” system in the PC coordinates. The staff’s decision of the probable leak detection is supported by comparison of the current location of the operating point (on the “fault trajectory”) with the boundaries of the confidence ellipsoid. It must be emphasized that due to daily and seasonal changes of heat/electricity demands, the process variables have substantially greater variability than in the examples of batch processes studied in literature. Despite those real challenges for the MPCA method, numerical examples confirmed that the presented approach was able to foresee the leaks earlier than the operator, typically 3–5 days before the boiler shutdown. The presented methodology may be useful in implementation of an on-line system, developed to improve safety and maintenance of boilers in a thermal-electrical power plant.

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Section: Current Approaches To Fault Detection In a Pipeline System Omentioning

confidence: 99%

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Swiercz

Mroczkowska²

2020

Sensors

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“…The feedforward NN is natural for its learning and recognizing capacity, which comprises an interconnected gathering of artificial neurons. [30][31][32] It is trained to provide a specific capacity by changing the estimations of connections (weights and biases) between the neurons of various layers. The ANN utilized as a part of this work comprises the input layer, hidden layer, and the output layer.…”

Section: Phase 1: Ann Predictionmentioning

confidence: 99%

A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

Karthikumar

Kumar

Muneeswaran

2019

Measurement and Control

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Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers. It is fundamental to recognize and distinguish the power quality disturbances in the distribution system. To recognize and distinguish the power quality disturbances, the development of high protection schemes is required. This paper presents an optimal protection scheme for power quality event prediction and classification in the distribution system. The proposed protection scheme combines the performance of both the salp swarm optimization and artificial neural network. Here, artificial neural network is utilized in two phases with the objective function of prediction and classification of the power quality events. The first phase is utilized for recognizing the healthy or unhealthy condition of the system under various situations. Artificial neural network is utilized to perceive the system signal’s healthy or unhealthy condition under different circumstances. In the second phase, artificial neural network performs the classification of the unhealthy signals to recognize the right power quality event for assurance. In this phase, the artificial neural network learning method is enhanced by utilizing salp swarm optimization based on the minimum error objective function. The proposed method performs an assessment procedure to secure the system and classify the optimal power quality event which occurs in the distribution system. At that point, the proposed work is executed in the MATLAB/Simulink platform and the performance of the proposed system is compared with different existing techniques like Multiple Signal Classification-Artificial Neural Network (MUSIC-ANN), and Genetic Algorithm - Artificial Neural Network (GA-ANN). The comparison results demonstrate the superiority of the SSO-ANN technique and confirm its potential to power quality event prediction and classification.

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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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Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks

Cited by 69 publications

References 33 publications

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

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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