A novel plugged tube detection and identification approach for final super heater in thermal power plant using principal component analysis

Yu, Jungwon; Yoo, Jaeyeong; Jang, Jaeyel; Park, June Ho; Kim, Sungshin

doi:10.1016/j.energy.2017.02.154

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…For example, main steam temperature may show abnormal variations due to the following several factors: malfunction of attemperators, excess air ratio, fouling formed on outer surfaces of superheater, and slag attached to outer surfaces of waterwall. To avoid the failures due to extremely high metal temperature, it is indispensable to control the steam temperature precisely [6]. When the steam temperature is lower than its rated value, moisture content in the main steam may increase; this may cause erosion of steam turbines.…”

Section: Drum-type Steam Boiler In Thermal Power Plantmentioning

confidence: 99%

“…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%

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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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Section: Drum-type Steam Boiler In Thermal Power Plantmentioning

confidence: 99%

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

Self Cite

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“…The process control data can provide sufficient information for effective tube leakage detection [8]. Jungwon et al [9] utilized the thermocouples sensors data mounted on the final superheater outlet header of an 870 MW coal-fired power plant and proposed a principal component analysis (PCA)-based tube leakage detection approach. The proposed method could successfully detect tube leakage.…”

Section: Introductionmentioning

confidence: 99%

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

2021

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Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

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“…Kornel et al [19] used ANN to develop models for early tube leak detection that are based on process variables. Jungwon et al [20] used data from thermocouple sensors mounted on the final superheater (FSH) tube bank for plugging tube detection and identification. As these signals are obtained for the process control system, this method eliminates the need to install expensive devices specifically for the intelligent fault detection system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

Khalid

Lim

Kim

et al. 2020

Sensors

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Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

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A novel plugged tube detection and identification approach for final super heater in thermal power plant using principal component analysis

Cited by 17 publications

References 27 publications

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

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

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

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