Method of state identification of rolling bearings based on deep domain adaptation under varying loads

Abstract

Large amounts of labelled vibration data of rolling bearings are difficult to acquire in full during operating conditions under varying loads. Moreover, a large divergence in data distribution exists between source and target domains for the same state. A multiple-state identification method for rolling bearings under varying loads is proposed. The deep domain adaptation method integrates the convolutional and pooling theory with the deep belief network (DBN) that enables the construction of a convolutional Gaussian–Bernoulli DBN, which is used to extract the deep generalised features from the frequency-domain amplitudes of the rolling bearings. The weighted mixed kernel is then used instead of the single kernel to improve the joint distribution adaptation, which is used to process the features of both the labelled source domain and the unlabelled target domain for domain adaptation, and reduce the distribution divergence. Finally, the k-nearest neighbour algorithm is used for identification. Experimental results show that the proposed method can make full use of unlabelled data, mine the deep features of vibration signals, and reduce the divergence between data of the same state. In resolving the multiple-state identification of rolling bearings under varying loads, a higher accuracy is attained in the identification