针对风电塔筒疲劳寿命难以有效预测的问题，提出了基于声发射特征参数融合退化曲线和PSO-LSTM （粒子群-长短时记忆）寿命预测模型的疲劳寿命预测方法。首先在实验室条件下基于声发射技术采集风电塔筒原材料（Q355E）疲劳全过程的声发射信号，从原始信号的时域与频域特征中提取特征参量，之后应用PCA （主成分分析）方法对特征参量进行融合，将第一主成分作为融合之后的特征曲线；最后以LSTM模型为基础，使用PSO算法优化模型参数，并建立PSO-LSTM模型来进行寿命预测。结果表明，使用优化模型的预测精度比单一模型的要高，具备一定的工业前景。

Aiming at the problem that the fatigue life of wind power tower is difficult to predict effectively, a fatigue life prediction method based on acoustic emission characteristic parameter fusion (degradation curve) and PSO-LSTM (particle swarm optimization-long-short term memory) life prediction model was proposed. Firstly, acoustic emission signals of the whole process of fatigue of raw materials (Q355E) of wind power tower were collected based on acoustic emission technology under laboratory conditions, and characteristic parameters were extracted from the time-domain and frequency-domain characteristics of the original signals. Then PCA(principle component analysis) method was used to fuse the characteristic parameters, and the first principal component was used as the characteristic curve after fusion. Finally, based on LSTM model, PSO algorithm was used to optimize model parameters, and PSO-LSTM model was established to predict life. The results showed that the prediction accuracy of the optimized model was higher than that of the single model, which had a certain industrial prospects.