Domain Invariant and Consistent Ordinal Representation Learning for Remaining Useful Life Prediction of Bearings

Recently, the remaining useful life (RUL) prediction of bearings driven by deep learning (DL) technology has gained massive attention. However, there are significant differences in the degradation processes from different bearings, which makes it difficult to adapt the model to unseen data. Moreover, the training paradigm of DL assumes that samples are independent, ignoring the ordinal relationships within feature space. This work considers that generalizable features in RUL prediction should possess two characteristics: domain invariance and continuous ordering. To this end, a domain invariant and consistent ordinal representation learning (DICORL) method is proposed for RUL estimation of bearings. DICORL employs maximum mean discrepancy to align feature distributions of multiple bearings in the training set. To alleviate the overfitting problem of the model in given domains, a distribution encoding-decoding framework is designed to project aligned features into the latent space that is constrained by a Gaussian mixture distribution. Moreover, ordinal loss and local consistency loss are constructed to encourage the model to learn ordered and locally consistent low-dimensional manifolds. Extensive experiments indicate that DICORL obtains superior prognostic performance compared to other domain generalization approaches.