Research and Development of Fault Diagnosis Methods for Liquid Rocket Engines

Abstract

Currently, considerable efforts are being focused on the development of reusable rockets and smart rockets due to the heavy requirements of future next-generation aerospace transportation. Safety, low-launching cost, and repeatability are expected from liquid rocket for fulfilling the big dreams of space transportation, exploration, and travelling. Therefore, research on fault detection of the liquid rocket engines (LRE) is critical for satisfying the above claims. Therefore, a comprehensive survey on the research and development of fault diagnosis systems and methods for the liquid rocket engines is presented. First, development history of liquid rocket engine diagnostic systems is reviewed thoroughly. Then three broad headings of the fault detection approaches of liquid rocket engines are divided through the summary and analysis of the existing methods, including approaches using signal processing, model-driven approach, and approach using artificial intelligence (AI). Then the paper discusses the concrete algorithms according to the classification features of the algorithms. In the end, the future developments of the fault detection approaches are presented, which will mainly pay attention to the reusability and intelligence of the rockets.

Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling

“…Chelouati et al [24] proposed an extended Kalman filter to predict the SoH of a LPRE in case of crack propagation in the combustion chamber. Wang et al [25] proposed a detailed survey of diagnosis techniques applied to rocket engines or rocket engines subsystems. Most of the research performed in this specific field concerns on-line or real-time approaches in the domain of Health Aware Control.…”

Numerical Simulation of Chemical Propulsion Systems: Survey and Fundamental Mathematical Modeling Approach

“…The mass flow rate of combustion gas ( ṁc ) through the nozzle throat area can be expressed as: (18) where C d , A n , P c , k c , R c , and T c are the discharge coefficient, nozzle throat area, pressure, heat specific ratio, gas constant, and temperature of the combustion gas, respectively.…”

“…Furthermore, in the RLV case, as with aircraft engines, the RLV propulsion system must be ready for the next flight quickly because its inspection process before launch generally requires some disassembly, considering the tradeoff between reliability and maintenance costs. As a result, the fault simulation using the system-level model-based simulation is essential to reduce the number of tests by substituting the actual experiment and to generate a database for the failure mode and effect analysis (FMEA) for constructing a reliable health monitoring system [12][13][14][15][16][17][18]. Therefore, the above advantages of developing a reliable simulation program can be summarized:…”

Preemptive Failure Detection using Contamination-Based Stacking Ensemble in Missiles

“…Studies of macro-level approaches include the development of condition monitoring systems to evaluate and enhance the reliability of missiles stored and exposed in the field [3], and reliability prediction using condition monitoring data [4]. Furthermore, with advancements in computational techniques, studies utilizing machine learning algorithms have been conducted, including missile lifespan prediction [5], failure characteristics and anomaly analysis of liquid rocket engine components [6][7][8][9], fault detection and diagnosis in solid rocket motors [10], and anomaly detection methods using flight data [11].…”