Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method

Rolling element bearings are widely used in rotating machines and their faults can lead to heavy investment and productivity losses so that the fault diagnosis of bearing is very important for guaranteeing a high performance transmission. However, despite the marvelous success of data-driven models, the most existing intelligent methods have a great limitation: the training data and testing data are under the same working condition. Therefore, to promote the successful applications of intelligent fault diagnosis, domain adaptation is employed in this paper. Specifically, we proposed a framework based on multilayer multiple kernel variant of Maximum Mean Discrepancy. For guaranteeing the stable results and improving the accuracy, the kernel method is introduced for replacing the high dimensional map of Maximum Mean Discrepancy. This makes features from different domains are close to each other in the Reproducing Kernel Hilbert Space. Besides, the features from two different domains of each feature layer both participate in the domain adaptation. Two bearing datasets are adopted to verify the effectiveness of the proposed method. The experimental results show that the proposed method can not only break the limitation of existing methods, but also achieve a superior performance comparing with related method. (C) 2019 Elsevier B.V. All rights reserved.