基于CNN-Transformer encoder-BiLSTM模型的轴承剩余寿命预测OA

To address the problem that the bearing degradation process exhibited strong nonlinearity and long-term dependencies were difficult to be effectively modeled under complex operating conditions,a remaining useful life prediction method based on an improved convolutional neural network(CNN)-Transformer encoder-bidirectional long short-term memory(BiLSTM)model was proposed.In this method,the CNN block was used to focus on local information for better feature extraction.The improved Transformer encoder was used to introduce three different attention masks so that the attention calculation process only focused on the important part of the long-term information.The BiLSTM was used to focus on long-term dependencies of all information.The accuracy of the model was validated on the C-MAPSS and XJTU-SY datasets.Experimental results show that the model achieves better prediction performance than other existing methods,and demonstrates better stability after Gaussian noise is added.