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

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.

Inspec keywords: fault diagnosis; production engineering computing; belief networks; feature extraction; rolling bearings; convolution; vibrations; Gaussian processes

Other keywords: deep domain adaptation method; labelled vibration data; deep belief network; data distribution; unlabelled target domain; multiple-state identification method; rolling bearings; labelled source domain; deep generalised feature extraction; frequency-domain amplitudes; convolutional Gaussian–Bernoulli DBN; joint distribution adaptation

Subjects: Other topics in statistics; Statistics; Knowledge engineering techniques; Data handling techniques; Production engineering computing; Combinatorial mathematics; Industrial applications of IT; Combinatorial mathematics; Mechanical components; Vibrations and shock waves (mechanical engineering)

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-smt.2019.0043
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