# Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
## 1 Introduction  
In today's industrial world, industrial robots are widely used and play an important role in the production of large and medium-sized companies. In view of the complex structure and serious failures of industrial robots, a sudden failure of industrial robots can cause delays in construction because of the loss of production [1]. To ensure the safety of robots, intelligent and efficient fault detection is vital [2]. Data-driven methods have been widely used, but knowledge-based fault detection methods have not yet been explored [3, 4, 5]. Using knowledge graphs to represent knowledge in the field of mechanical fault detection can improve the efficiency and accuracy of mechanical fault detection. We collected mechanical fault detection logs as a fault relevant corpus. There are numerous logical relationships between events in the fault relevant corpus which can be helpful for the fault diagnosis. For example, the high humidity in the operating environment of the motor causes the motor to generate excessive heat after starting. There is an event logic relationship in this sentence. However, most knowledge graphs generally take nominal entities and their relationships as the research objects, lack the mining of logical knowledge of events, and have poor reasoning ability. Using an event logic knowledge graph as a knowledge representation technique, we can track how events evolve and further examine what knowledge is. The event logic knowledge graph is more powerful, more in line with the objective laws of human cognition, and more useful for practical purposes. To construct a graph, it is necessary to extract events from a large number of fault relevant corpora according to the ontology model. However, the event classes defined by most ontology construction methods are typically divided into fault phenomenon classes and fault cause classes, which results in coarse knowledge granularity, which is not conduciveto knowledge reasoning. Consequently, the best way to obtain valuable fault detection knowledge is to decompose event classes through finer-grained ontology construction and to recognize entities effectively. As a result of the small corpus size, technical terms, and sparse entities, existing deep learning methods perform poorly in event argument entity recognition on mechanical fault relevant corpora. First of all, many existing methods enhance the recognition effect by improving the input features through vocabulary enhancement, which is easily susceptible to information loss. Second, the blurred lexical boundaries of units in Chinese can lead to word segmentation errors and propagation problems. Lastly, there are numerous relations among the event argument entities in the mechanical fault relevant corpus. Incorporating the relationship information between event argument entities can help the model to better understand the semantic structure, but most methods do not combine the entity-relationship information for prediction, and are unable to infer semantic meanings through the relationship. In light of the above motivation, we propose MF-GCN, which is a model for recognizing event argument entities on fault relevant corpora. By combining the multi-text feature and entity-relationship of event argument entities, MF-GCN is able to achieve ideal results in event argument entity recognition on fault relevant corpora. MF-GCN does not employ a vocabulary enhancement method, but instead uses character-level embedding as the basic unit. MF-GCN utilizes pretrained Word2 vec embedding layers to obtain character-level vector representations. In terms of multi-feature fusion, MF-GCN mainly extracts local context features, context features, and context salience features. Local context features are important in determining entity semantics. Context features enable each entity in a sentence to capture long-distance dependency information, and context salience features can integrate local context information with long-distance dependency information better.