A novel method based on deep transfer unsupervised learning network for bearing fault diagnosis under variable working condition of unequal quantity

Abstract

Recently, intelligent fault recognition means have been progressed rapidly and have attained marvelous achievement. Most of them have an assumption where those source and target domains have similar distributions. However, actual working conditions of bearing are variable, which makes the source domain and target domain data present large distribution discrepancy. In this paper, a new method named dilated convolution deep belief network-dynamic multilayer perceptron (DCDBN-DMLP) is proposed for bearing fault recognition under alterable running states. Firstly, dilated convolution deep belief network (DCDBN) is proposed to extract transferable characteristics from raw vibrational dataset of bearings under variable running conditions. The divergence between source domains and target domains is large because the dataset generally is gathered from multi-condition environments. Then, the multilayer domain adaptation and pseudo label technology are adopted to alleviate the cross and unequal quantity domains. Finally, dynamic multilayer perceptron (DMLP) is proposed to classify bearing faults, which is connected to DCDBN in a progressive manner. The performance of this proposed DCDBN-DMLP model is validated by three transfer tasks with bearing fault dataset. Experimental results present that the abundant transferable features can be learned by this proposed method, and its classification accuracies significantly outperform other methods by comparison.