# Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
## 3 Methodology
### Model Structure  
The MF-GCN consists of three layers: a feature fusion layer, a stacked BiLSTM layer, and a BiGCN entity relation inference layer. Figure 2(a) illustrates the model structure of this paper. Figure 1: Mechanical fault detection ontology construction schematic. Figure 2: Example of argument entity and relation annotations and relation diagrams for sample sentences. The sentence in (**a**) means that the motor winding is affected with damp and the insulation aging makes the shell live. An entity follows the BIO pattern, in which B indicates the start of the entity, I indicates the middle of the entity, and O indicates none. A relationship is connected by an entity tail to another entity tail. (**b**) shows the entity relationship of the sentence in (**a**). It shows that the motor is composed of winding and shell. The shell is charged due to damp and insulation aging. Feature Fusion Layer:Context Features.Context features enable entities to capture information about long-distance dependencies. For a text sentence \(T=t_{1}\), \(t_{2}\),..., \(t_{n}\), \(t_{i}\) is the character in the text sentence. Through the Word2 vec embedding layer to obtain the character vector representation of the sentence \(C=(x_{1},x_{2},\ldots,x_{n})\), after the vector C is Dropout processed, it is input to Bi-LSTM so that each character of the sentence obtains the context feature, and output this vector representation \(X=(o_{1},o_{2},\ldots,o_{n})\) with context features extracted. Context Salient Features.Information on long-distance dependency and local context salient features can be combined better with context salient features. Using CNNs (Convolutional Neural Networks) for convolution operations, we obtain the vector representation Y of the salient context feature. Local Context Features.Both of the above features only consider character- level embeddings and do not consider radical features. In order to maximize the benefit of radical features, this paper proposes a method for extracting local context features using radical information. Local context features are important in determining entity semantics. For each character \(t_{i}\) in the text sentence \(T=\{t_{1},t_{2},\ldots,t_{n}\}\), extract the radical \(r_{i}\) of each character, and obtain the radical sequence \(R=r_{1}\), \(r_{2}\),..., \(r_{n}\) of the sentence. Then, through the radical embedding layer, the radical sequence \(R\) is transformed into the radical-level vector representation D. The radical-level vector D and the character-level vector C are merged, and after Dropout processing is performed, they are input into the CNN to extract the local context feature vector Z that combines the radical information and character information. Local context features allow each character to obtain context information from adjacent characters, and these features are important for recognizing event argument entities in fault detection datasets. The formula for calculating the feature vector Z is as follows:  
\[Z=Conv(D\oplus C) \tag{1}\]  
Figure 3: The structure of the model. (**a**) the text on the left means that high temperature causes the motor to heat up. (**b**) is the radical sequence of the left sentence. Stacked BiLSTM LayerThe feature vectors X, Y, and Z are merged and input into a stacked two-layer Bi-LSTM, and the output entity hidden layer represents the sequence \(H=(x_{1},\,x_{2},\ldots,\,x_{n})\). Figure 3b illustrates the specific structure of Bi-LSTM stacked with two layers. By stacking stacked Bi-LSTMs, long-range dependencies can be captured, making up for the disadvantage that the Bi-GCN module, which will be used later, cannot encode long-range information. Finally, we input the output vector of the stacked Bi-LSTM into the conditional random field (CRF) to obtain the first-stage entity prediction result. BiGCN entity relation inference layerWe utilize expert-defined and labeled event-argument-entity relations in the dataset to construct graph structures from text sequences.