基于多尺度特征融合的滚动轴承寿命预测OA

Rolling bearings are commonly used components in mechanical equipment,and the effective prediction of the remaining useful life(RUL)of bearings plays an important role in formulating a reasonable maintenance plan,avoiding sudden downtime of mechanical equipment,and ensuring the safety of equipment.Traditional deep learning methods are difficult to extract multi-dimensional and multi-scale degradation features,which reduces the accuracy of RUL prediction.Meanwhile,there are uncertainties such as noise and model parameters,which make it difficult to meet the maintenance requirements for point prediction of RUL.In this paper,we propose an RUL prediction model called EEMD-AFP-FSBLformer,which integrates the FSBLformer network,attention feature pyramid(AFP),discrete wavelet transform(DWT),and ensemble empirical mode decomposition(EEMD).The low-frequency modal functions are firstly processed by EEMD decomposition with DWT noise reduction and bearing time-domain degradation features with the noise reduction processed high-frequency modal functions in order to produce more representative degradation features;then the degradation features are inputted into the AFP network in order to extract the multi-scale features;and finally,these degradation features are used as inputs to the FSBLformer model.The FSBLformer model's encoder incorporates the self-attention and feature attention mechanisms,while the decoder employs the bidirectional long short-term memory(BiLSTM)network,which improves the model's performance in time prediction and feature extraction.The experiments are conducted in different working conditions of the PHM2012 dataset and XJTU-SY dataset,and the comparative experimental analysis shows that the model has a high coefficient of determination of more than 94%,which can effectively extract the multi-scale degradation features of bearings and predict their RUL.