Digital Intelligence Monitor and Intelligent Diagnosis Technology for Health Condition of Turbine EH Oil System

With the deepening of industrial automation and intelligence, the health condition of steam turbines, as the key equipment of the power system, is particularly important. As a key component of the steam turbine, the health condition of the EH oil system plays a decisive role in the safety and stability of the entire power system. The working principle and common problems of EH oil system is reviewed, the root causes of common failures of EH oil system are analyzed, and the development and application of condition monitoring and fault diagnosis technology in power system are discussed, aiming at exploring the use of artificial intelligence and other technologies to monitor the condition and fault diagnosis of EH oil system. The design and application of digital intelligent monitoring and intelligent diagnosis technology in EH oil system is proposed. Through the analysis, it is concluded that: In the future, a large number of fault samples will be provided through digital and intelligent monitoring, intelligent diagnosis technology will be integrated, and intelligent technologies such as artificial intelligence, machine learning, and genetic algorithm will be used to carry out fault warning, fault diagnosis and health condition evaluation of the EH oil system, so as to improve the operational stability and safety of the EH oil system.

Authors

Institutions

Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1145/3756423.3756444
Primary Topic
Engineering Diagnostics and Reliability
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital Intelligence Monitor and Intelligent Diagnosis Technology for Health Condition of Turbine EH Oil System

Y. Zhang, Fuyuan Zhan, Xiong Wu, Yuejun Yang et al.
Engineering Diagnostics and Reliability
article

Digital Intelligence Monitor and Intelligent Diagnosis Technology for Health Condition of Turbine EH Oil System

Y. Zhang, Fuyuan Zhan, Xiong Wu, Yuejun Yang, Lihua Ao, Shi‐Yin Xiong
article en

Abstract

With the deepening of industrial automation and intelligence, the health condition of steam turbines, as the key equipment of the power system, is particularly important. As a key component of the steam turbine, the health condition of the EH oil system plays a decisive role in the safety and stability of the entire power system. The working principle and common problems of EH oil system is reviewed, the root causes of common failures of EH oil system are analyzed, and the development and application of condition monitoring and fault diagnosis technology in power system are discussed, aiming at exploring the use of artificial intelligence and other technologies to monitor the condition and fault diagnosis of EH oil system. The design and application of digital intelligent monitoring and intelligent diagnosis technology in EH oil system is proposed. Through the analysis, it is concluded that: In the future, a large number of fault samples will be provided through digital and intelligent monitoring, intelligent diagnosis technology will be integrated, and intelligent technologies such as artificial intelligence, machine learning, and genetic algorithm will be used to carry out fault warning, fault diagnosis and health condition evaluation of the EH oil system, so as to improve the operational stability and safety of the EH oil system.

Thermal Power Research Institute (CN), National Power (United Kingdom) (GB)
Openalex Percentile: Top 71%
Engineering Diagnostics and Reliability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.