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Sensor Failure Management in Liquid Rocket Engine using Artificial Neural Network

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Title Sensor Failure Management in Liquid Rocket Engine using Artificial Neural Network
 
Creator Flora, J Jessi
Auxillia, D Jeraldin
 
Subject Bayesian Regularisation algorithm
Liquid rocket engine
Qualification test
Regression
Sensor
 
Description 1024-1027
This paper presents a novel Artificial Neural Network based Fault Detection, Isolation and Substitution
(ANN-FDIS)algorithm for faulty sensor measurement in Liquid Rocket Engine (LRE). Fault detection and isolation are done by residual and fault flag logics and the trained multilayer perceptron model Artificial Neural Network (ANN) substitutes faulty sensor measurement. Data for ANN training, testing and validation are extracted from qualification and validation hot tests of LRE. Regression (R) and Mean Square Error (MSE) are considered for evaluating the ANN. During validation of this study, the faulty sensor is identified, isolated and data substituted from other input parameters with an error less than ±0.7%. This unique scheme does not require accurate modeling of the complicated LRE as well as sensor hardware redundancy which adds weight, space and power to rockets.
 
Date 2020-11-04T07:05:54Z
2020-11-04T07:05:54Z
2020-11
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/55621
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source JSIR Vol.79(11) [November 2020]