Correlation feature distribution matching for fault diagnosis of machines

The variation of working conditions makes the probability distribution of the source domain data and the target domain data differ greatly, which leads to the performance degradation of conventional fault diagnosis methods. Transfer learning is an effective tool to deal with this issue. However, existing methods still have shortcomings since they generally treat the two distributions equally and thus cannot effectively adjust the relative importance of the two distributions. What's more, they focus more on the probability distribution but neglect to align the features of the two domains. To this end, this study proposes a framework called correlated feature distribution matching (CFDM) to efficiently achieve cross-domain fault diagnosis. Firstly, CFDM finds the correlation information between the source and target domains by the correlation feature matching, and then performs second-order feature alignment for the two domains, thus reducing the difficulty of feature adaptation. This can not only reduce the original feature distance between two domains, but also effectively avoid the feature distortion caused by the loss of the original key information in the feature transformation, so as to obtain the correlation features. Secondly, CFDM takes into account both the marginal distribution and the conditional distribution, and dynamically adjusts the relative importance of the two distributions of the correlation features through the feature dynamic adaptation. This can precisely match the weights of the two distributions and further reduce the distribution difference between the two domains. Finally, the validity and reliability of the proposed CFDM are verified on three bearing test rigs.