Anomaly detection of steam turbine with hierarchical pre‐warning strategy-Reference-Cited by-同舟云学术

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Anomaly detection of steam turbine with hierarchical pre‐warning strategy

Published:2022-03-22 Issue:12 Volume:16 Page:2357-2369
ISSN:1751-8687
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation Trans & Dist

Author:

Yao Kun¹,Fan Shuangshuang²,Wang Ying³,Wan Jie²,Yang Donghui⁴,Cao Yong¹

Affiliation:

1. School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China

2. School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China

3. College of Power and Energy Engineering Harbin Engineering University Harbin China

4. School of Economics and Management Southeast University Nanjing China

Funder

China Postdoctoral Science Foundation

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/gtd2.12452

Reference49 articles.

1. Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks

2. A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation

3. An LSTM based method for stage performance degradation early warning with consideration of time-series information

4. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

5. Yao K. Shi J. Luo H. et al.Turbine load control instability fault and its diagnosis method with big data fusion model. In:2019 IEEE International Conference on Power Data Science (ICPDS).Taizhou China pp.63–66(2019)

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3. Machine learning for fault analysis in rotating machinery: A comprehensive review;Heliyon;2023-06

4. A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines;Mathematics;2023-05-12

5. Predictive Maintenance and Fault Monitoring Enabled by Machine Learning: Experimental Analysis of a TA-48 Multistage Centrifugal Plant Compressor;Applied Sciences;2023-01-30

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