This paper presents a novel fault diagnosis method based on data fusion for a reaction flywheel of the satellite attitude system. Different from most traditional fault diagnosis techniques, the proposed solution simultaneously accomplishes fault detection and identification within parallel fusion blocks. The core of this method is independent fusion block, which uses a generalized ordered weighted average (GOWA) operator to complement the characteristics of output data from long short-term memory (LSTM) neural network and discrete wavelet transform (DWT) so as to enhance the reliability and rapidity of decision-making. Moreover, minibatch normalization is selected to address the problem of covariate shift, realize the adaptive processing of the dynamic information in the original data, and improve the convergence speed of the network. With the high-fidelity model of the reaction flywheel, three common faults are, respectively, injected to collect experimental data. Extensive experiment results show the efficacy of the proposed method and the excellent performance achieved by LSTM and DWT.

中文翻译：

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基于LSTM和DWT的卫星反应飞轮数据融合故障诊断方法。

本文提出了一种基于数据融合的卫星姿态系统反应飞轮故障诊断方法。与大多数传统故障诊断技术不同，所提出的解决方案在并行融合块内同时完成故障检测和识别。该方法的核心是独立融合块，该融合块使用广义有序加权平均值（GOWA）运算符来补充长短期记忆（LSTM）神经网络和离散小波变换（DWT）的输出数据特征，从而增强决策的可靠性和快速性。此外，选择小批量归一化来解决协变量偏移问题，实现原始数据中动态信息的自适应处理，提高网络的收敛速度。使用反动飞轮的高保真模型，分别注入三个常见故障以收集实验数据。大量的实验结果证明了该方法的有效性以及LSTM和DWT所获得的优异性能。