The remaining useful life (RUL) prediction has an important guiding role in the preventive maintenance of wind turbine generators (WTGs). In this article, a real-time dynamic perception model of the RUL prediction is proposed for WTGs, which contains the multi-state parameters processing, the performance degradation analysis, the performance degradation prediction and the RUL prediction. First, the degradation process is fitted to the random distribution on the basis of the principal component analysis (PCA) of the multi-state parameters. Secondly, the multivariate time series samples and the corresponding performance degradation amount are all brought into the 1-dimensional convolution neural network (1D-CNN) for the regression analysis. Then, the bidirectional long short memory (Bi-LSTM) neural network is set to predict the amount of performance degradation in time series to obtain the future trend of performance degradation. Finally, the result of the RUL prediction is obtained by contrasting the setting threshold of the performance degradation. The results show that the proposed model has lower regression analysis error and degradation prediction error than the single deep learning and traditional models, and can obtain more accurate and reliable RUL prediction results.

剩余使用寿命（RUL）预测在风力发电机（WTG）的预防性维护中具有重要的指导作用。本文提出了一种风力发电机组RUL预测的实时动态感知模型，包括多状态参数处理、性能退化分析、性能退化预测和RUL预测。首先，基于多状态参数的主成分分析 (PCA)，将退化过程拟合到随机分布。其次，将多元时间序列样本和相应的性能退化量全部带入一维卷积神经网络（1D-CNN）进行回归分析。然后，设置双向长短记忆（Bi-LSTM）神经网络以预测时间序列的性能退化量，以获得性能退化的未来趋势。最后，通过对比性能下降的设置阈值得到RUL预测的结果。结果表明，与单一的深度学习和传统模型相比，本文提出的模型具有更低的回归分析误差和退化预测误差，可以获得更准确可靠的RUL预测结果。