Corpus Construction and Entity Recognition for the Field of Industrial Robot Fault Diagnosis

Abstract

The fault logs record the fault information generated during the operation process of industrial robots. It contains a large amount of fault knowledge and solution information. It is necessary to extract this information and build the fault diagnosis knowledge graph of industrial robots, which can support remote fault diagnosis of industrial robots without human help. At present, the research of fault diagnosis knowledge graph is still relatively scarce. In this paper, we propose a method of named entity recognition for extracting the knowledge of industrial robot fault diagnosis. The contribution of our paper is to establish the fault field dataset Fault-Data, propose the ontology concept of the fault diagnosis field, and obtain a good field recognition effect through the verification of the entity recognition model of fault diagnosis. Experimental results show that the F value of named entity recognition reaches 91.99%, which provides a certain reference significance for subsequent knowledge extraction and knowledge graph construction.