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Open AccessArticle

Data Driven Leakage Detection and Classification of a Boiler Tube

School of IT Convergence, University of Ulsan, Ulsan 44610, Korea
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Appl. Sci. 2019, 9(12), 2450; https://doi.org/10.3390/app9122450
Received: 7 May 2019 / Revised: 5 June 2019 / Accepted: 13 June 2019 / Published: 15 June 2019
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification. View Full-Text
Keywords: Acoustic emissions; boiler tube; deep learning; deep neural network; leakage detection; wavelet packet transform Acoustic emissions; boiler tube; deep learning; deep neural network; leakage detection; wavelet packet transform
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Sohaib, M.; Kim, J.-M. Data Driven Leakage Detection and Classification of a Boiler Tube. Appl. Sci. 2019, 9, 2450.

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