@inproceedings{db4541b8def64784ab312abfe0ea0d9a,
title = "APPLICATION OF IMPROVED RESIDUAL DETECTION FOR SENSOR FAULT DIAGNOSIS OF LIQUID ROCKET ENGINE",
abstract = "This paper presents an approach for detecting and isolating faults (FDI) in liquid rocket engine sensors. The approach involves incorporating model identification into the engine modeling process, specifically through a combination of subspace and prediction error methods. The estimation coefficient matrix of the state-space model is first obtained using the subspace identification method, followed by re-estimation using the prediction error method. The paper also introduces an improved residual detection algorithm for sensor fault isolation that utilizes a bank of Kalman filters designed for each sensor. Through residual analysis, faults can be isolated and identified. The approach is verified using simulation data from the space shuttle main engine (SSME), with evaluation results provided for sensor faults at various operating conditions.",
keywords = "Kalman filter, Sensor fault diagnosis, liquid rocket engine modeling, subspace model identification",
author = "Yufeng Su and Sun, \{Ruo Bin\} and Xuefeng Chen",
note = "Publisher Copyright: {\textcopyright} 2023 Proceedings of the International Congress on Sound and Vibration. All rights reserved.; 29th International Congress on Sound and Vibration, ICSV 2023 ; Conference date: 09-07-2023 Through 13-07-2023",
year = "2023",
language = "英语",
series = "Proceedings of the International Congress on Sound and Vibration",
publisher = "Society of Acoustics",
editor = "Eleonora Carletti",
booktitle = "Proceedings of the 29th International Congress on Sound and Vibration, ICSV 2023",
}