Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis

Abstract

The advance of knowledge graphs can bring tangible benefits to the fault detection of industrial robots. However, the construction of the KG for industrial robot fault detection is still in its infancy. In this paper, we propose a top-down approach to constructing a knowledge graph from robot fault logs. We define the event argument classes for fault phenomena and fault cause events as well as their relationship. Then, we develop the event logic ontology model. In order to construct the event logic knowledge extraction dataset, the ontology is used to label the entity and relationship of the fault detection event argument in the corpus. Additionally, due to the small size of the corpus, many professional terms, and sparse entities, a model for recognizing entities for robot fault detection is proposed. The accuracy of the entity boundary determination of the model is improved by combining multiple text features and using the relationship information. Compared with other methods, this method can significantly improve the performance of entity recognition of the dataset.