Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine

Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine

Fault detection and diagnosis can improve safety and reliability of gas turbines. Current studies on gas turbine fault detection and diagnosis mainly focus on the case of abundant fault samples. However, fault data are rare or even unavailable for gas turbines, especially newly-run gas turbines. Aim...

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Bibliographic Details
Main Authors: Mingliang Bai, Jinfu Liu, Yujia Ma, Xinyu Zhao, Zhenhua Long, Daren Yu
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Energies
Subjects:
fault detection and diagnosis
anomaly detection
three-shaft marine gas turbine
long short-term memory (LSTM) network
deep learning
normal pattern group
Online Access:https://www.mdpi.com/1996-1073/14/1/13

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https://www.mdpi.com/1996-1073/14/1/13

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