A Generalized Autonomous Power Plant Fault Detection Model Using Deep Feature Extraction and Ensemble Machine Learning DOI Creative Commons
Salman Khalid, Muhammad Muzammil Azad, Heung Soo Kim

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 342 - 342

Published: Jan. 22, 2025

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

Language: Английский

Leak detection in pipelines based on acoustic emission and growing neural gas network utilizing unlabeled healthy condition data DOI
Anil K. Mishra,

Jogin Dhebar,

Bimal Das

et al.

Flow Measurement and Instrumentation, Journal Year: 2025, Volume and Issue: unknown, P. 102816 - 102816

Published: Jan. 1, 2025

Language: Английский

Citations

1
Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect DOI

Jianwu Chen,

Xiao Wu, Zhibo Jiang

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116857 - 116857

Published: Jan. 1, 2025

Language: Английский

Citations

1
A Generalized Autonomous Power Plant Fault Detection Model Using Deep Feature Extraction and Ensemble Machine Learning DOI Creative Commons
Salman Khalid, Muhammad Muzammil Azad, Heung Soo Kim

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 342 - 342

Published: Jan. 22, 2025

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

Language: Английский

Citations

0