Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55621
Title: Sensor Failure Management in Liquid Rocket Engine using Artificial Neural Network
Authors: Flora, J Jessi
Auxillia, D Jeraldin
Keywords: Bayesian Regularisation algorithm;Liquid rocket engine;Qualification test;Regression;Sensor
Issue Date: Nov-2020
Publisher: NISCAIR-CSIR, India
Abstract: 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.
Page(s): 1024-1027
URI: http://nopr.niscair.res.in/handle/123456789/55621
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(11) [November 2020]

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