A mixture non-parametric regression prediction model with its application in the fault prediction of rocket engine thrust

IF 1.8 Q3 ENGINEERING, INDUSTRIAL
Hao Xiang
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Abstract

Purpose It is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is an important indicator for its health monitoring. By predicting the changing value of the thrust, it can be judged whether the engine will fail at a certain time. However, the thrust is affected by various factors, and it is difficult to establish an accurate mathematical model. Thus, this study uses a mixture non-parametric regression prediction model to establish the model of the thrust for the health monitoring of a liquid rocket engine. Design/methodology/approach This study analyzes the characteristics of the least squares support vector regression (LS-SVR) machine . LS-SVR is suitable to model on the small samples and high dimensional data, but the performance of LS-SVR is greatly affected by its key parameters. Thus, this study implements the advanced intelligent algorithm, the real double-chain coding target gradient quantum genetic algorithm (DCQGA), to optimize these parameters, and the regression prediction model LSSVRDCQGA is proposed. Then the proposed model is used to model the thrust of a liquid rocket engine. Findings The simulation results show that: the average relative error (ARE) on the test samples is 0.37% when using LS-SVR, but it is 0.3186% when using LSSVRDCQGA on the same samples. Practical implications The proposed model of LSSVRDCQGA in this study is effective to the fault prediction on the small sample and multidimensional data, and has a certain promotion. Originality/value The original contribution of this study is to establish a mixture non-parametric regression prediction model of LSSVRDCQGA and properly resolve the problem of the health monitoring of a liquid rocket engine along with modeling the thrust of the engine by using LSSVRDCQGA.
混合非参数回归预测模型及其在火箭发动机推力故障预测中的应用
目的建立准确可靠的故障预测模型对液体火箭发动机的健康监测具有重要意义。液体火箭发动机的推力是其健康监测的重要指标。通过预测推力的变化值,可以判断发动机在某一时刻是否会发生故障。但推力受多种因素影响,难以建立精确的数学模型。因此,本研究采用混合非参数回归预测模型建立了用于液体火箭发动机健康监测的推力模型。设计/方法/方法本研究分析最小二乘支持向量回归(LS-SVR)机器的特点。LS-SVR适用于小样本和高维数据的建模,但LS-SVR的关键参数对其性能影响很大。为此,本研究采用先进的智能算法——实双链编码目标梯度量子遗传算法(DCQGA)对这些参数进行优化,并提出回归预测模型LSSVRDCQGA。然后将该模型应用于某液体火箭发动机的推力建模。仿真结果表明:使用LS-SVR对测试样本的平均相对误差(ARE)为0.37%,而使用LSSVRDCQGA对相同样本的平均相对误差为0.3186%。本文提出的LSSVRDCQGA模型对于小样本和多维数据的故障预测是有效的,具有一定的推广意义。本研究的原创性贡献在于建立了LSSVRDCQGA混合非参数回归预测模型,并利用LSSVRDCQGA对发动机推力进行建模,较好地解决了液体火箭发动机健康监测问题。
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来源期刊
Journal of Quality in Maintenance Engineering
Journal of Quality in Maintenance Engineering Engineering-Safety, Risk, Reliability and Quality
CiteScore
4.00
自引率
13.30%
发文量
24
期刊介绍: This exciting journal looks at maintenance engineering from a positive standpoint, and clarifies its recently elevatedstatus as a highly technical, scientific, and complex field. Typical areas examined include: ■Budget and control ■Equipment management ■Maintenance information systems ■Process capability and maintenance ■Process monitoring techniques ■Reliability-based maintenance ■Replacement and life cycle costs ■TQM and maintenance
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