An enhancement deep feature fusion method for rotating machinery fault diagnosis

It is meaningful to automatically learn the valuable features from the raw vibration data and provide accurate fault diagnosis results. In this paper, an enhancement deep feature fusion method is developed for rotating machinery fault diagnosis. Firstly, a new deep auto-encoder is constructed with denoising auto-encoder (DAE) and contractive auto-encoder (CAE) for the enhancement of feature learning ability. Secondly, locality preserving projection (LPP) is adopted to fuse the deep features to further improve the quality of the learned features. Finally, the fusion deep features are fed into softmax to train the intelligent diagnosis model. The developed method is applied to the fault diagnosis of rotor and bearing. The results confirm that the proposed method is more effective and robust compared with the existing methods. (C) 2016 Elsevier B.V. All rights reserved.