Research on multi-granularity imbalanced knowledge condition monitoring for mechanical equipment based on hierarchical ELM in multi-entropy space

Abstract

Condition monitoring is essentially a complex multi-classification task that requires struggling with multiple conditions and imbalanced data. To address this issue, this study proposes a hierarchical cognition method that utilizes multi-entropy feature representation to capture the state of equipment under complex working conditions. Firstly, this study proposes a feature modeling method for mechanical equipment based on multi-entropy space, utilizing the cloud model as a space component. Secondly, the study uses multi-entropy feature data to develop a hierarchical Extreme Learning Machine (HELM) fault identification model for complex working conditions and data imbalance inspired by human hierarchical cognitive behavior. The feature data are decomposed into four levels, namely speed, load, abnormality, and fault, respectively. Additionally, a hierarchical feature selection mechanism is designed to improve the efficiency of HELM. Finally, comparative experiments are carried out on two platforms. Experiments show that the If factors of the two datasets in the multi-entropy space reach 0.6888 and 10.4881, respectively. At the same time, in the classification experiment for the imbalanced data, the classification accuracy reaches 90.68 when the fault samples are 10. The results show that the multi-entropy feature created in this study has a specific representation ability for mechanical states. Furthermore, the proposed hierarchical extreme learning method can effectively identify faults under multiple conditions and data imbalance backgrounds.