Lu Jiantao, Yang Kuangzhi, Zhang Peng, Wu Wei, Li Shunming
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, China.
AECC Guiyang Engine Design Research Institute, Guiyang 550081, China.
Sensors (Basel). 2025 Mar 26;25(7):2066. doi: 10.3390/s25072066.
Trend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (LSTM) neural network under multi-operating conditions. Firstly, a condition recognition technology is constructed to automatically identify the operating conditions based on the predetermined judgment conditions, and vibration signal features are adaptively divided into three typical operating conditions, namely, the idling operating condition, the starting operating condition and the utmost operating condition. The features of original signals are extracted to reduce the impacts of signal fluctuations and noise preliminarily. Secondly, enhanced SAN is used to normalize and denormalize the features to alleviate non-stationary factors. To improve prediction accuracy, an filter is adopted to extract the trend term of the features, which can effectively reduce the overfitting of SAN to local information. Moreover, the slice length is quantitatively estimated by the fixed points in filtering, and a tail amendment technology is added to expand the applicable range of enhanced SAN. Finally, an LSTM-based forecasting model is constructed to forecast the normalized data from enhanced SAN, serving as input during denormalization. The final results under different operating conditions are the output from denormalization. The validity of the proposed method is verified using the test data of an aircraft engine. The results show that the proposed method can achieve higher forecasting accuracy compared to other methods.
趋势预测和早期异常预警对于避免飞机发动机故障或事故至关重要。本研究提出了一种基于增强切片级自适应归一化（SAN）的趋势预测方法，该方法在多运行条件下使用长短期记忆（LSTM）神经网络。首先，构建一种工况识别技术，基于预定的判断条件自动识别运行工况，并将振动信号特征自适应地分为三种典型运行工况，即怠速运行工况、启动运行工况和极限运行工况。提取原始信号的特征以初步减少信号波动和噪声的影响。其次，使用增强的SAN对特征进行归一化和反归一化，以减轻非平稳因素。为提高预测精度，采用一种滤波器提取特征的趋势项，这可以有效减少SAN对局部信息的过拟合。此外，通过滤波中的固定点对切片长度进行定量估计，并添加尾部修正技术以扩大增强SAN的适用范围。最后，构建基于LSTM的预测模型来预测来自增强SAN的归一化数据，在反归一化期间用作输入。不同运行条件下的最终结果是反归一化的输出。使用飞机发动机的测试数据验证了所提方法的有效性。结果表明，与其他方法相比，所提方法能够实现更高的预测精度。
