Article
A Method for Real-Time Fault Detection of Liquid Rocket
Engine Based on Adaptive Genetic Algorithm Optimizing Back
Propagation Neural Network
Huahuang Yu and Tao Wang *
Citation: Yu, H.; Wang, T. A Method
for Real-Time Fault Detection of
Liquid Rocket Engine Based on
Adaptive Genetic Algorithm
Optimizing Back Propagation Neural
Network. Sensors 2021, 21, 5026.
https://doi.org/10.3390/s21155026
Academic Editors: Kim Phuc Tran,
Athanasios Rakitzis and Khanh T.
P. Nguyen
Received: 4 June 2021
Accepted: 21 July 2021
Published: 24 July 2021
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School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China;
yuhh6@mail2.sysu.edu.cn
* Correspondence: wangt339@mail.sysu.edu.cn
Abstract:
A real-time fault diagnosis method utilizing an adaptive genetic algorithm to optimize a
back propagation (BP) neural network is intended to achieve real-time fault detection of a liquid
rocket engine (LRE). In this paper, the authors employ an adaptive genetic algorithm to optimize a
BP neural network, produce real-time predictions regarding sensor data, compare the projected value
to the actual data collected, and determine whether the engine is malfunctioning using a threshold
judgment mechanism. The proposed fault detection method is simulated and verified using data
from a certain type of liquid hydrogen and liquid oxygen rocket engine. The experiment results show
that this method can effectively diagnose this liquid hydrogen and liquid oxygen rocket engine in
real-time. The proposed method has higher system sensitivity and robustness compared with the
results obtained from a single BP neural network model and a BP neural network model optimized
by a traditional genetic algorithm (GA), and the method has engineering application value.
Keywords:
liquid rocket engine; liquid hydrogen and liquid oxygen rocket engine; genetic algorithm;
back propagation neural network; fault detection
1. Introduction
With the advent of key aerospace programs, such as deep space exploration, crewed
spaceflight, lunar exploration, and space station construction, LRE serves as the central
power unit and component of the launch vehicle propulsion system. Its reliability and
safe operation have become a focus of people’s attention [
1
]. Effective fault detection and
management systems for LRE can help reduce the likelihood of system failure during
operation and prevent unwarranted property damage.
Existing fault detection and diagnosis methods for LRE are often classified into three
categories: model-driven methods, data-driven methods, and methods based on artificial
intelligence [
1
–
3
]. The model-driven method needs to establish a system model according
to the law of system operation. The degree of conformity between the established model
and the actual circumstance determines the accuracy of the diagnosis results. However,
LRE is a complicated nonlinear system with significant nonlinearity, non-stationarity, and
uncertainty, making it challenging to develop an appropriate system model. This method
is frequently used in conjunction with others. Ref. [
4
] performed real-time fault diagnosis
on LRE based on the autoregressive moving average (ARMA) model. The data-driven
method analyzes the engine’s output signal with respect to the relationship between the
system’s output and the fault. This approach requires a large amount of high-quality data
to be supported. However, even for the same type of engine, the faults shown may vary
due to the engine’s working conditions. This method is more ideal for fault diagnostics and
alarming during the engine’s steady condition. Ref. [
5
] established an adaptive correlation
algorithm and envelope method for real-time fault detection and alarm during steady-state
and startup processes of LRE. Methods based on artificial intelligence mainly include
Sensors 2021, 21, 5026. https://doi.org/10.3390/s21155026 https://www.mdpi.com/journal/sensors
