Review of Launch Vehicle Engine PHM Technology and Analysis Methods Research

Lin, Ruliang; Yang, Jialin; Huang, Liyong; Liu, Zhiwen; Zhou, Xuehua; Zhou, Zhiguo

doi:10.3390/aerospace10060517

Cited by 3 publications

(2 citation statements)

References 64 publications

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“…The 1st failure mode is the efficiency decrease in turbine components, which is caused by other failures that occur in turbine components such as rotor rubbing, centrifugal pump cavitation, etc. An efficiency factor is introduced to simulate a decrease in power leading to a decrease in rotational speed and a decrease in the work conducted by centrifugal pumps for fault simulation, which can be modeled as (1).…”

Section: System-level Failure Simulation Model Of Lrementioning

confidence: 99%

“…The reusable Liquid Rocket Engine (LRE) is a decisive subsystem for the reliability, economy and dispatchability of a reusable launch vehicle. The Prognosis and Health Management (PHM) technologies based on in situ sensor measurements are useful tools for realizing the increasing reliability requirements of reusable LREs [1]. Meanwhile, data-driven fault diagnosis algorithms are currently the hottest research area in the PHM community, which develops fault diagnosis algorithms using sensor measurements data from multi-physics modeling, hot-fire test-run experiments or actual launch telemetry data.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine

Ma,

Zhong,

Zhai

et al. 2024

Aerospace

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There are hundreds of various sensors used for online Prognosis and Health Management (PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for inflight control purposes in LRE, it is practical to optimal placement of redundant sensors for improving the diagnosability and economics of PHM systems. To strike a balance between sensor cost, real-time performance and diagnosability of the fault diagnosis algorithm in LRE, this paper proposes a novel Optimal Sensor Placement (OSP) method. Firstly, a Kernel Extreme Learning Machine-based (KELM) two-stage diagnosis algorithm is developed based on a system-level failure simulation model of LRE. Secondly, hierarchical diagnosability metrics are constructed to formulate the OSP problem in this paper. Thirdly, a Hierarchy Ranking Evolutionary Algorithm-based (HREA) two-stage OSP method is developed, achieving further optimization of Pareto solutions by the improved hypervolume indicator. Finally, the proposed method is validated using failure simulation datasets and hot-fire test-run experiment datasets. Additionally, four classical binary multi-objective optimization algorithms are introduced for comparison. The testing results demonstrate that the HREA-based OSP method outperforms other classical methods in effectively balancing the sensor cost, real-time performance and diagnosability of the diagnosis algorithm. The proposed method in this paper implements system-level OSP for LRE fault diagnosis and exhibits the potential for application in the development of reusable LREs.

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Section: System-level Failure Simulation Model Of Lrementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine

Ma,

Zhong,

Zhai

et al. 2024

Aerospace

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Recent advances in the mechanical and electrical performance of carbon nanotube fibers under various strain-rate loadings

Zhou,

Gu,

Zhou

et al. 2023

Materials Today Chemistry

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Particle-Filter-Based Fault Diagnosis for the Startup Process of an Open-Cycle Liquid-Propellant Rocket Engine

Cha,

Ko,

Park

2024

Sensors

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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.

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Review of Launch Vehicle Engine PHM Technology and Analysis Methods Research

Cited by 3 publications

References 64 publications

A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine

A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine

Recent advances in the mechanical and electrical performance of carbon nanotube fibers under various strain-rate loadings

Particle-Filter-Based Fault Diagnosis for the Startup Process of an Open-Cycle Liquid-Propellant Rocket Engine

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