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.

中文翻译：

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基于人工神经网络的液体火箭发动机传感器故障管理

本文提出了一种新颖的基于人工神经网络的故障检测，隔离和替代算法（ANN-FDIS），用于液体火箭发动机（LRE）的故障传感器测量。故障检测和隔离由残差和故障标记逻辑完成，训练有素的多层感知器模型人工神经网络（ANN）替代了故障传感器的测量。用于ANN训练，测试和验证的数据是从LRE的验证和验证热测试中提取的。考虑回归（R）和均方误差（MSE）来评估ANN。在本研究的验证期间，有故障的传感器会被识别，隔离并从其他输入参数中替代数据，误差小于±0.7％。这种独特的方案不需要对复杂的LRE进行精确建模，也不需要传感器硬件冗余，从而增加了重量，