The existing aeroengine instability precursor detection methods can be summarized as applying advanced signal processing technologies to various signals from the compressor test rig rather than the whole engine. Besides, these methods seriously depend on the artificial designed feature and threshold and also ignore the limit on the sensors onboard. Thus, with the help of the powerful feature extraction ability of the deep neural network, a real-time surge prediction method based on the multi-branch feature fusion neural network (MBFFNN) is proposed. First, the dataset can be obtained by using overlapping slices to divide surge test data into a sample sequence and using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to label each sample precisely. Second, for each sample, the time-domain statistical parameters are calculated and the recurrence plot is obtained by using phase space reconstruction. Finally, the MBFFNN with mixed data type input is designed, and its performance is evaluated by the generated dataset. The experimental results show that compared with multilayer perceptron (MLP), long short-term memory (LSTM), and deep residual network (DRN), MBFFNN has the best performance on two datasets for different surge tests, which demonstrates that the proposed method for surge prediction can accurately judge the state of the aeroengine, identify the instability precursor before the surge, and give an early warning in advance.

中文翻译：

---

基于多分支特征融合神经网络的涡轴发动机喘振实时预测

现有的航空发动机不稳定性前兆检测方法可以概括为将先进的信号处理技术应用于来自压缩机试验台而不是整个发动机的各种信号。此外，这些方法严重依赖于人为设计的特征和阈值，也忽略了对板载传感器的限制。因此，借助深度神经网络强大的特征提取能力，提出了一种基于多分支特征融合神经网络（MBFFNN）的实时浪涌预测方法。首先，可以通过使用重叠切片将浪涌测试数据划分为样本序列，并使用具有自适应噪声的完全集成经验模态分解（CEEMDAN）来精确标记每个样本，从而获得数据集。其次，对于每个样本，计算时域统计参数，利用相空间重构得到递归图。最后，设计了混合数据类型输入的MBFFNN，并通过生成的数据集对其性能进行了评估。实验结果表明，与多层感知器 (MLP)、长短期记忆 (LSTM) 和深度残差网络 (DRN) 相比，MBFFNN 在不同浪涌测试的两个数据集上具有最佳性能，这表明所提出的方法对于喘振预测可以准确判断航空发动机的状态，识别喘振前的不稳定前兆，提前预警。其性能由生成的数据集评估。实验结果表明，与多层感知器 (MLP)、长短期记忆 (LSTM) 和深度残差网络 (DRN) 相比，MBFFNN 在不同浪涌测试的两个数据集上具有最佳性能，这表明所提出的方法对于喘振预测可以准确判断航空发动机的状态，识别喘振前的不稳定前兆，提前预警。其性能由生成的数据集评估。实验结果表明，与多层感知器 (MLP)、长短期记忆 (LSTM) 和深度残差网络 (DRN) 相比，MBFFNN 在不同浪涌测试的两个数据集上具有最佳性能，这表明所提出的方法对于喘振预测可以准确判断航空发动机的状态，识别喘振前的不稳定前兆，提前预警。