As a representative integrated system for power-by-wire (PBW) systems, Electro-hydrostatic actuator (EHA) has series of advantages such as high power density, compactness, and high efficiency, which is one of the important development directions of future hydraulic system field. However, due to its high integration and high reliability requirements, it is challenging to conduct degradation studies in short period of time with limited data samples. For this type of high integrated mechatronics system, Prognostics and Health Management (PHM) is one of the key works to ensure its safety and reliability, especially the performance degradation prediction presented in this paper. To deal with the small size of EHA data, a time-based data enhancement method for expanding the performance data set is proposed based on Time Generative Adversarial Network (TimeGAN). Considering the complex of working state and system performance, the relationship between the EHA operation data and its health indicator is then analyzed using the CNN-BiLSTM-Attention model, so as to generate the health indicator combine with TimeGAN synthesis data. Finally, CNN-BiLSTM-Attention model with multi-input channels is developed, and EHA data as well as TimeGAN synthesized EHA data are incorporated into the model. The results show that this method can greatly improve the prediction accuracy of EHA performance, and provide a novel method for performance degradation prediction of integrated mechatronic system.

中文翻译：

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基于时间序列生成对抗网络的 CNN-BiLSTM-Attention EHA 退化预测方法

电液静压执行器（EHA）作为线控动力（PBW）系统的代表性集成系统，具有功率密度高、结构紧凑、效率高等一系列优点，是未来液压的重要发展方向之一。系统字段。然而，由于其高集成度和高​​可靠性要求，在短时间内利用有限的数据样本进行降解研究具有挑战性。对于此类高集成机电一体化系统，预测与健康管理（PHM）是确保其安全性和可靠性的关键工作之一，特别是本文提出的性能退化预测。针对EHA数据量较小的情况，基于时间生成对抗网络（TimeGAN）提出了一种基于时间的数据增强方法来扩展性能数据集。考虑到工作状态和系统性能的复杂性，然后利用CNN-BiLSTM-Attention模型分析EHA运行数据与其健康指标之间的关系，从而结合TimeGAN合成数据生成健康指标。最后，开发了具有多输入通道的CNN-BiLSTM-Attention模型，并将EHA数据以及TimeGAN合成的EHA数据合并到模型中。结果表明，该方法可以极大地提高EHA性能的预测精度，为集成机电系统性能退化预测提供一种新的方法。