Fault diagnosis based on deep learning by extracting inherent common feature of multi-source heterogeneous data

Fault diagnosis can provide a basic guarantee for safe operation of industrial equipment. Deep learning has attracted much attention from experts in the field of fault diagnosis because of its powerful feature representation ability. But traditional deep learning methods cannot well extract common feature from multi-source heterogeneous data which is the inherent character of the monitored object. Using only one kind of heterogeneous data for deep learning fault diagnosis will inevitably result in poor diagnosis accuracy. Aiming at this problem, this article proposes a deep common feature extraction method by designing a fusion network with alternating optimization mechanism. The rough features extracted independently from two kinds of heterogeneous data are used to train the designed fusion network in an alternative optimization way. A new loss function required by alternative optimization is established; thus, all the networks can be tuned globally. The deep common features of multi-source heterogeneous data can be well extracted by alternating optimization training process of the fusion network, which improves the accuracy of deep learning fault diagnosis method. Experiments for rolling bearings fault diagnosis testify the effectiveness of the proposed algorithm.