Cha Jihyoung, Ko Sangho, Park Soon-Young
Centre for Aeronautics, Cranfield University, Cranfield MK43 0AL, UK.
Department of Smart Air Mobility, Korea Aerospace University, 76 Hanggongdaehang-ro, Deogyang-gu, Goyang-si 10540, Republic of Korea.
Sensors (Basel). 2024 Apr 27;24(9):2798. doi: 10.3390/s24092798.
This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and diagnosis (FDD) algorithm for the startup process, the algorithm in this study is composed of a nonlinear filter to generate residuals, a residual analysis, and a multiple-model (MM) approach to detect and diagnose faults from the residuals. In contrast to the previous study, this study makes use of the modified cumulative sum (CUSUM) algorithm, widely used in change-detection monitoring, and a particle filter (PF), which is theoretically the most accurate nonlinear filter. The algorithm is confirmed numerically using the CUSUM and MM methods. Subsequently, the FDD algorithm is compared with an algorithm from a previous study using a Monte Carlo simulation. Through a comparative analysis of algorithmic performance, this study demonstrates that the current PF-based FDD algorithm outperforms the algorithm based on other nonlinear filters.
本研究介绍了一种基于粒子滤波的开式循环液体推进剂火箭发动机（LPRE）故障诊断算法。该算法作为启动过程的一种基于模型的方法，占发动机故障的30%以上。与先前用于启动过程的故障检测与诊断（FDD）算法类似，本研究中的算法由一个用于生成残差的非线性滤波器、残差分析以及一种用于从残差中检测和诊断故障的多模型（MM）方法组成。与先前的研究相比，本研究使用了广泛应用于变化检测监测的修正累积和（CUSUM）算法以及理论上最精确的非线性滤波器——粒子滤波器（PF）。使用CUSUM和MM方法对该算法进行了数值验证。随后，使用蒙特卡罗模拟将该FDD算法与先前研究中的一种算法进行了比较。通过对算法性能的对比分析，本研究表明当前基于PF的FDD算法优于基于其他非线性滤波器的算法。
