Fault Pattern Recognition of Bearing Under off Design Condition Based on Knowledge Transfer Network
In view of the fact that the fault diagnosis method based on transfer learning could not make full use of the data in the target domain and required smooth operation conditions,a fault pattern recognition method of variable working condition bear-ing based on fusion knowledge migration network was proposed.The input instantaneous speed was input into the sparse automat-ic encoder as the working condition information,so that the target domain information could be fully utilized,and the whole op-eration information could be incorporated into the model for training instead of only using the local vibration data set,and the risk of negative transfer in the learning process was greatly reduced through the model training.Then,the deep convolution neu-ral network was used to extract features from the original vibration,and the fusion knowledge transfer model was established by combining the two knowledge transfer models.Finally,the experimental results on the rolling bearing test-bed show that the method can achieve effective fault identification under variable working conditions.
Rolling BearingMigration LearningFault Pattern RecognitionSparse Automatic Encoder
万方数据
维普
