A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network

Normal operation of bearing is the key to ensure the reliability and security of rotary machinery, so that bearing fault diagnosis is quite significant. However, the large amount of data collected by modern data acquisition system and time-varying working conditions make it hard to diagnose the fault using traditional methods To break the predicaments, we propose a new intelligent fault diagnosis framework inspired by the infinitesimal method. The proposed model including three parts can ignore the effect of different rotational speeds. Firstly, the sample is segmented and every segment dimension is extended by input network to ensure the adequate information memory space. Secondly, the classification information is stored and transferred in the long short-term memory (LSTM) network and output to the third part. In this process, the working condition information is ignored because of the gate units function. Finally, the likelihood is given by output network to classify the health conditions. Besides, we propose a loss function combining all the output of every time step and employ dropout to train the model, which increase the training efficiency and diagnosis ability. The bearing datasets under time-varying speeds and loads are used to verify the proposed method. The application result shows that our method has higher accuracy with simpler structure, and is superior to the traditional method in bearing fault diagnosis. Moreover, we give a physical interpretation of the proposed model.