Academic Journal
Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
| العنوان: | Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis |
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| المؤلفون: | Senye Chen, Lianglun Cheng, Jianfeng Deng, Tao Wang |
| المصدر: | Applied Sciences, Vol 12, Iss 23, p 12359 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | event logic knowledge graph, fault detection, event argument entity recognition, multi-feature Fusion, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/12/23/12359; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app122312359 |
| URL الوصول: | https://doaj.org/article/71a46a3e315646ba98b02914cd00f0b6 |
| رقم الانضمام: | edsdoj.71a46a3e315646ba98b02914cd00f0b6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20763417 |
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| DOI: | 10.3390/app122312359 |