A Generalized Autonomous Power Plant Fault Detection Model Using Deep Feature Extraction and Ensemble Machine Learning

Salman Khalid, Muhammad Muzammil Azad and Heung Soo Kim ()
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Salman Khalid: Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Muhammad Muzammil Azad: Department of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Heung Soo Kim: Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea

Mathematics, 2025, vol. 13, issue 3, 1-19

Abstract: Ensuring operational reliability and efficiency in steam power plants requires advanced and generalized fault detection methodologies capable of addressing diverse fault scenarios in boiler and turbine systems. This study presents an autonomous fault detection framework that integrates deep feature extraction through Convolutional Autoencoders (CAEs) with the ensemble machine learning technique, Extreme Gradient Boosting (XGBoost). CAEs autonomously extract meaningful and nonlinear features from raw sensor data, eliminating the need for manual feature engineering. Principal Component Analysis (PCA) is employed for dimensionality reduction, enhancing computational efficiency while retaining critical fault-related information. The refined features are then classified using XGBoost, a robust ensemble learning algorithm, ensuring accurate fault detection. The proposed model is validated through real-world case studies on boiler waterwall tube leakage and motor-driven oil pump failure in steam turbines. Results demonstrate the framework’s ability to generalize across diverse fault types, detect anomalies at an early stage, and minimize operational downtime. This study highlights the transformative potential of combining deep feature extraction and ensemble machine learning for scalable, reliable, and efficient fault detection in power plant operations.

Keywords: steam power plants; fault detection; ensemble machine learning technique; convolutional autoencoder; principal component analysis; extreme gradient boosting; autonomous feature extraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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