  Bearing failure is the most common failure mode in rotating machinery and can
result in large financial losses or even casualties. However, complex
structures around bearing and actual variable working conditions can lead to
large distribution difference of vibration signal between a training set and a
test set, which causes the accuracy-dropping problem of fault diagnosis. Thus,
how to improve efficiently the performance of bearing fault diagnosis under
different working conditions is always a primary challenge. In this paper, a
novel bearing fault diagnosis under different working conditions method is
proposed based on domain adaptation using transferable features(DATF). The
dataset of normal bearing and faulty bearings are obtained through the fast
Fourier transformation(FFT) of raw vibration signals under different motor
speeds and load conditions. Then we reduce marginal and conditional
distributions simultaneously across domains based on maximum mean
discrepancy(MMD) in feature space by refining pseudo test labels, which can be
obtained by the Nearest-Neighbor(NN) classifier built on training data, and
then a robust transferable feature representation for training and test domains
is achieved after several iterations. With the help of the NN classifier
trained on transferable features, bearing fault categories are identified
accurately in final. Extensive experiment results show that the proposed method
under different working conditions can identify the bearing faults accurately
and outperforms obviously competitive approaches.
