A parallel ensemble optimization and transfer learning based intelligent fault diagnosis framework for bearings

Abstract

Transfer learning (TL) is an important method to accurately identify the bearing health status in cross-domain and ensure the safe operation of machinery. With the advancement in research, it will become a trend to choose different neural networks or optimization functions to improve and re-model fault diagnosis methods. However, the variants of these fault diagnostic methods are less capable of generalizing input dimensions and do not significantly increase demand for machinery expertise. The idea of ensemble learning solves the problem of low generalization. In this research, a parallel ensemble optimization loss function and multi-source TL based model are proposed to solve the problem of unknown distribution difference between source domain and target domain, thus improving the generalization of optimization objectives. Firstly, based on the signal demodulation method, an adaptive input module is constructed to automatically select the input length from the original vibration signal. Secondly, a TL network with low-dimensional features reuse is constructed to achieve weight and bias sharing. Thirdly, a parallel ensemble optimization loss function is developed to align the data whose distribution is unknown between source and target domains. Finally, two cases with multi-source, unsupervised, and cross-domain TL are used to verify the performance of the proposed method. The average accuracy in case 1 and case 2 is 99.81 % and 99.17 % respectively. It is proved that the proposed method can not only get rid of the limitation of manual input length setting, but also overcome the limitation of optimization function, which is more effective than the existing intelligent fault diagnosis models.