Title: Bearing fault diagnosis under varying working condition based on domain adaptation,
Abstract: Traditional intelligent fault diagnosis of rolling bearings work well only
under a common assumption that the labeled training data (source domain) and
unlabeled testing data (target domain) are drawn from the same distribution.
When the distribution changes, most fault diagnosis models need to be rebuilt
from scratch using newly recollected labeled training data. However, it is
expensive or impossible to annotate huge amount of training data to rebuild
such new model. Meanwhile, large amounts of labeled training data have not been
fully utilized yet, which is apparently a waste of resources. As one of the
important research directions of transfer learning, domain adaptation (DA)
typically aims at minimizing the differences between distributions of different
domains in order to minimize the cross-domain prediction error by taking full
advantage of information coming from both source and target domains. In this
paper, we present one of the first studies on unsupervised DA in the field of
fault diagnosis of rolling bearings under varying working conditions and a
novel diagnosis strategy based on unsupervised DA using subspace alignment (SA)
is proposed. After processed by unsupervised DA with SA, the distributions of
training data and testing data become close and the classifier trained on
training data can be used to classify the testing data. Experimental results on
the 60 domain adaptation diagnosis problems under varying working condition in
Case Western Reserve benchmark data and 12 domain adaptation diagnosis problems
under varying working conditions in our new data are given to demonstrate the
effectiveness of the proposed method. The proposed methods can effectively
distinguish not only bearing faults categories but also fault severities.