To solve the shortcomings of traditional mechanical fault diagnosis methods that requires manual extraction of features

an end to end fault diagnosis method based on multiple attention-convolutional neural networks-bidirectional gated recurrent unit（MA-CNN-BiGRU） was proposed. First

the original time domain data was input into convolutional neural networks（CNN） to extract feature and reduce dimensionality preliminary

and then the results were reorganized into bidirectional gated recurrent unit（BiGRU）

which could effectively solve the problem that BiGRU was difficult to deal with long sequence data. The open-source bearing data set of Case Western Reserve University in the United States was used for training

and the optimal number of convolutional layers and the optimal sample length reduction ratio were determined to be 2 and 1/8 respectively. At the same time

by adding convolutional block attention module（CBAM） and sequence attention module（SAM） to CNN and BiGRU respectively

the models ability to capture key information is further strengthened. Finally

the actual measurement results of the diesel engine fault vibration signal experiment show that the MA-CNN-BiGRU model can achieve end-to-end fault diagnosis

and compared with the variational mode decomposition-kernel fuzzy C-means clustering algorithm（VMD-KFCM）

VMD-BP neural networks（VMD-BPNN） and one-dimensional CNN

the fault diagnosis performance of the MA-CNN-BiGRU is better.