Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree

Abstract

Bearing health condition identification plays a crucial role in guaranteeing maximum productivity and reducing maintenance costs. In this article, a novel tensorial feature extraction approach called hierarchical multiscale symbolic dynamic entropy is developed, which can be used to assess the dynamic characteristic of the measured vibration data at different hierarchical layers and different scales. Besides, the influence of parameters in hierarchical multiscale symbolic dynamic entropy is investigated so as to select the optimal parameters. Then, a new multi-fault classifier called least squares support tensor machine–based binary tree is presented to achieve the fault identification automatically. In the least squares support tensor machine–based binary tree method, the divisibility measure strategy is constructed by two new separability measures (i.e. the average center distance of samples in one class, the center distance of samples between sub-class and global class). Finally, a novel intelligent fault diagnosis scheme based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree is developed, which is applied to analyze the experimental data of rolling bearing. The results indicate that the proposed scheme has a superior performance in health condition identification. Compared with the existing symbolic dynamic entropy–based fault diagnosis methods, the proposed method has higher diagnostic accuracy and better stability.