Detection of Boiler Tube Leakage Fault in a Thermal Power Plant Using K-means Algorithm based on Auto-Associative Neural Network

Kim, Kyu han; Lee, Heung seok; Park, June Ho

doi:10.1109/isap48318.2019.9065940

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) 2019

DOI: 10.1109/isap48318.2019.9065940

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Detection of Boiler Tube Leakage Fault in a Thermal Power Plant Using K-means Algorithm based on Auto-Associative Neural Network

Kyu han Kim

Heung seok Lee

June Ho Park

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2024

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Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

Abdul Karim,

Mohd Mustafah,

Zainal Abidin

2024

JST

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Boiler tube leaks significantly reduce the operational availability of power units, yet their early detection and prediction have not been fully realised in the industry. This paper introduces a novel approach employing deep feedforward neural networks for early detection of boiler tube leaks in pulverised coal-fired boilers. Early detection enhances repair planning, minimising downtime and production losses. It also improves monitoring and control of boiler tube failures, thereby optimising power plant operations and revenue. Diverse deep neural network models were developed and rigorously tested by leveraging 9 years of operational data (2012–2020). Exhaustive hyper-parameter optimisation, involving seven parameters, substantially improved predictive accuracy. By achieving training and testing accuracies of 82.8% to 99.3%, the study assessed their ability to detect boiler tube leaks over the same 9-year span, providing insights into leak detection capabilities. The models notably predicted all 12 identified tube leak events, averaging a 14-day lead time before boiler shutdown. In addition to leak prediction, a leak detection matrix was devised to analyse residual behaviour and reduce the likelihood of false alarms. However, the models’ predictive performance was observed to be limited to the following year, with satisfactory results for 2021 only. Incorporating the 2021 data into retraining significantly improved the predictions for 2022. The study concludes that while the models demonstrate robust short-term prediction capabilities, they require continuous retraining to maintain accuracy and relevance. This ongoing refinement is essential for keeping the models up-to-date and reliable in predicting future boiler tube leaks.

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Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

Abdul Karim,

Mohd Mustafah,

Zainal Abidin

2024

JST

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Detection of Boiler Tube Leakage Fault in a Thermal Power Plant Using K-means Algorithm based on Auto-Associative Neural Network

Cited by 1 publication

References 3 publications

Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

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