Fault state identification of rolling bearings based on deep transfer convolutional autoencoder and a new health indicator

Abstract

For the problem of bearing fault state identification, in recent years, the main research is how to distinguish the normal state and the fault state. However, it is still difficult to accurately identify the more detailed fault severity in the cross-working environment. Therefore, this paper proposes a new health indicator (HI) and deep transferable convolutional autoencoder network (DTCAE) to solve the problem of rolling bearing early fault monitoring and fault state recognition. Firstly, the envelope spectrum of the vibration signal is calculated, and then the ratio of the energy at the maximum amplitude of the envelope spectrum to other energies is obtained, and the final HI is obtained by weighting the standard deviation with it, which has good generalization and engineering practicability. HI is used for early bearing fault monitoring and classifying bearing fault status in source domain. Finally, aiming at the problem that the existing transfer learning methods do not consider the weakening of the target domain data features after domain adaptation, DTCAE is proposed to identify the bearing fault state. Specifically, in addition to the distribution difference loss and classification loss commonly seen in transfer learning models, a decoder is also added after feature extraction to calculate the difference between the reconstructed data of the target domain and the original data, and the feature distribution difference between the reconstructed source domain and the target domain is also calculated. This allows the source-domain bearing and the target-domain bearing features to be matched in such a way that both not only have the same feature distribution, but also retain the degradation trend characteristics of the target-domain bearing. Experiments in three challenging cases have achieved good results and are superior to some existing methods.