Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation Using Feature Transfer Learning

Abstract

Bearings, as universal components, have been widely used in the important position of rotating machinery. However, due to the distribution divergence between training data and test data caused by variable working conditions, such as different rotation speeds and load conditions, most of the fault diagnosis models built during the training stage are not applicable for the detection in the test stage. The models dramatically lead to the performance degradation for fault classification. In this paper, a novel bearing fault diagnosis method, domain adaptation by using feature transfer learning (DAFTL) under variable working conditions, is proposed to solve this performance degradation issue. The dataset of normal bearings and faulty bearings are obtained via the fast Fourier transformation of raw vibration signals, under different motor speeds and load conditions. Then, the marginal and conditional distributions are reduced simultaneously between training data and test data by refining pseudo test labels based on the maximum mean discrepancy and domain invariant clustering in a common space. Ultimately, a transferable feature representation for training data and test data is achieved. With the help of the nearest-neighbor classifier built on the transferable features, bearing faults are identified in this common space. Extensive experimental results show that the DAFTL can identify the bearing fault accurately under variable working conditions and outperforms other competitive approaches.