Bearing fault diagnosis of cross-working grinding mill based on transfer learning
A one-dimensional convolution transfer learning method based on multi-core maximum mean diffe-rence is proposed.Firstly,one-dimensional convolutional networks are utilized to directly extract fault feature information from the original vibration signals.Secondly,an adversarial strategy migration technique is em-ployed to assist the network in extracting common features between the two domains.Finally,the multi-core maximum mean difference is used to evaluate the distance between the source domain and target domain,ena-bling extraction of domain invariant features and facilitating transfer learning under four working conditions of the bearing dataset from Case Western Reserve University.Compared with traditional methods,the proposed approach can enhance fault classification accuracy by 6%,which has a good application prospect.
bearing faulttransfer learningmulti-core maximum mean differencefault diagnosis
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