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首页> 外文期刊>Journal of aerospace engineering >Novel Radial Basis Function Network Based on Dynamic Time Warping and Kalman Filter for Real-Time Monitoring of Supersonic Inlet Flow Patterns
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Novel Radial Basis Function Network Based on Dynamic Time Warping and Kalman Filter for Real-Time Monitoring of Supersonic Inlet Flow Patterns

机译：基于动态时间翘曲和卡尔曼滤波器的新型径向基函数网络，用于超声入口流动模式的实时监控

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As the basis of protection control, supersonic inlet plays an important role in a supersonic air-breathing propulsion system. To reduce the negative effects of buzz flow on the supersonic inlet and ensure safe and stable operation, it is of great significance to develop methods for monitoring the flow patterns. Traditionally, several manually engineered features are extracted as indicators to evaluate the operation status, but this process can be heavily dependent on professional experience and is time-consuming. This paper proposes a novel network called DTW-RBF-KF which integrates dynamic time warping (DTW) and the Kalman filter (KF) into a radial basis function (RBF) network architecture to directly determine flow patterns from the dynamic sensor signals. The proposed network replaces the Euclidean distance in the static RBF kernels with DTW distance, and exploits the flexible alignment ability of DTW to deal with the temporal distortions. The secondorder Levenberg-Marquardt optimization algorithm then is employed to allow the efficient training of the proposed network. To improve the classification performance when the network structure is fixed, the KF is applied as a postprocessing technique to linearly convert the predicted output of RBF into a value closer to the true output. Experimental results demonstrated that the proposed DTW-RBF-KF network has the highest average monitoring accuracy, 94.10%, and requires less calculation compared with all competitive methods. The average time required to test a sample of DTW-RBF-KF network is 1.19 ms, which is shorter than the sample duration, and can be exploited for real-time applications. The addition of a linear KF module further increases the monitoring accuracy to a higher level with negligible computation. The proposed method has better comprehensive performance for monitoring the flow patterns of supersonic inlet in terms of monitoring accuracy and real-time performance. (C) 2021 American Society of Civil Engineers.
机译：作为保护控制的基础，超音速入口在超音速空气呼吸推进系统中起重要作用。为了减少嗡嗡声对超音速入口上的嗡嗡声的负面影响，并确保安全稳定的操作，开发用于监测流动模式的方法具有重要意义。传统上，提取了几种手动工程化特征作为评估操作状态的指标，但这种过程可能严重依赖于专业体验，并且是耗时的。本文提出了一种名为DTW-RBF-KF的新型网络，其将动态时间翘曲（DTW）和卡尔曼滤波器（KF）集成到径向基函数（RBF）网络架构中，以直接从动态传感器信号确定流量模式。所提出的网络用DTW距离替换静态RBF内核中的欧几里德距离，利用DTW的灵活对准能力来处理时间扭曲。然后采用二阶Levenberg-Marquardt优化算法来允许提出网络的有效培训。为了提高网络结构固定的分类性能，KF被应用为后处理技术，以将预测输出的RBF的预测输出转换为更接近真实输出的值。实验结果表明，建议的DTW-RBF-KF网络具有最高的平均监测精度，94.10％，与所有竞争方法相比，需要较少计算。测试DTW-RBF-KF网络样本所需的平均时间为1.19ms，短于样本持续时间，并且可以用于实时应用程序。添加线性KF模块进一步将监视精度提高到更高水平，其计算可忽略不计。在监测精度和实时性能方面，该方法具有更好的全面性能，用于监测超音速入口的流量模式。 （c）2021年美国土木工程师协会。

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