Model-based supervised sensor placement optimization to detect propellant leak in a liquid rocket engine

Omata, Noriyasu; Satoh, Daiwa; Tsutsumi, Seiji; Kawatsu, Kaname; Abe, Masaharu

doi:10.1016/j.actaastro.2022.02.009

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…The second stage is a fault detection technology using signal processing and modeling analysis developed with the development of sensor technology; the third stage uses the core intelligent fault detection technology, such as data mining, deep learning, and information fusion. Scholars have different views on the classification of LRE fault diagnosis techniques [3,9,12,25,36,37]. In this paper, LRE fault detection approaches are divided into three types: signal processing-based approaches, model-driven approaches, and artificial intelligence-based approaches, which are shown in Figure 1.…”

Section: Summary Of State Of Fault Detection Technologymentioning

confidence: 99%

Research and Development of Fault Diagnosis Methods for Liquid Rocket Engines

Wang

Ding

2022

Aerospace

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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.

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Section: Summary Of State Of Fault Detection Technologymentioning

confidence: 99%

Research and Development of Fault Diagnosis Methods for Liquid Rocket Engines

Wang

Ding

2022

Aerospace

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“…Specific to aerospace systems, there are also some related works. Omata et al [36] employed a greedy approach utilizing the detection performance score from multivariate supervised analysis for sensor placement optimization to identify propellant leaks in an LRE. Yang et al [37] reported a two-step strategy of non-probabilistic multi-objective optimization for load-dependent sensor placement with interval uncertainties to determine the final sensor configuration from a Pareto solution, and the accuracy of the proposed method was verified using an example involving space docking module in Space Power Satellites (SPSs).…”

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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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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