Abstract
Knowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction method is proposed. Firstly, we define event arguments of fault phenomenon and fault cause events, define event argument classes and relation between classes, and construct an event logic knowledge ontology model. According to the event logic knowledge ontology, the fault diagnosis event argument entity and relation in the corpus are labeled, and an event logic knowledge extraction dataset is formed. Secondly, an event argument entity and relation joint extraction model is proposed. Using stacked bidirectional long short-term memory(BiLSTM) to obtain deep context features of text. As a supplement to stacked BiLSTM, self-attention mechanism extracts character dependency features from multiple subspaces, and uses conditional random field(CRF) to realize entity recognition. The character dependency features are mapped to the entity label weight embedding, and spliced with deep context features to extract relations. Bidirectional graph convolutional network(BiGCN) is introduced for relation inference, graph convolution features are used to update deep context features to perform joint extraction in the second phase. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed, which provides decision support for autonomous fault diagnosis of robot transmission system.
Highlights
With the rapid development of intelligent manufacturing, industrial robots play an increasingly important role in the production process of enterprises, which can perform production tasks more efficiently and accurately
Compared with Huang et al [64], the model proposed in this paper reduces 3.69% in EAER and improves 3.22% in EARE, which proves that the stacked BiLSTM proposed in this paper can obtain the deep context information of text and improve the accuracy of EARE, the two phase relation extraction can further improve the performance of EARE
A top-down event logic knowledge graph method is proposed based on the historical fault diagnosis event description text of robot transmission system
Summary
With the rapid development of intelligent manufacturing, industrial robots play an increasingly important role in the production process of enterprises, which can perform production tasks more efficiently and accurately. Jianfeng Deng et al.: Research on event logic knowledge graph construction method of robot transmission system fault diagnosis safe and stable operation of robot equipment and improve enterprise production efficiency. A self-attention-based stacked BiLSTM with label weight embedding and graph convolution network(SBALGN) is proposed for event argument entity and relation joint extraction. We collect a fault diagnosis event description corpus of robot transmission system, and a fine-grained event logic knowledge ontology is constructed. Through the above-proposed SBALGN, the event logic knowledge are extracted, and the event logic knowledge graph of robot transmission system fault diagnosis is initially constructed.
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