Power equipment defect intelligent diagnosis platform based on knowledge map

Hang Zhang; Rui Yang; Kang Liu; Guangdong Zhang; Jian Zhang

doi:10.1117/12.2684642

19 October 2023 Power equipment defect intelligent diagnosis platform based on knowledge map

Hang Zhang, Rui Yang, Kang Liu, Guangdong Zhang, Jian Zhang

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127095Q (2023) https://doi.org/10.1117/12.2684642
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

The defect data of electric power equipment generated during the production and operation of the electric power field is widely used for judging the defect degree of the equipment, assisting the inspection personnel to store production certificates, and facilitating the on-site maintenance of maintenance personnel. During the operation of electric power equipment, it is necessary to timely and effectively judge the defect degree of the equipment as to avoid a series of cascading failures caused by the untimely treatment of critical defective equipment. It affects the power production efficiency. This paper proposes an effective method for entity disambiguation. It finds that the knowledge map of power equipment defects lacks updating measures, and gives specific updating methods for different updating reasons. The model uses the attention mechanism to extract the word importance features in the defective text, uses the enhanced coding method to recode the digital information, and the fusion layer fuses the global semantic features, digital features, and word importance features; Finally, combined with knowledge atlas, the text description of power defects with given structure is realized. The model designed by comparison can more accurately identify the entity information in the defect text. Meanwhile, it can also achieve an end-to-end analysis effect of the defect degree of the power equipment. The research part based on historical defect text uses the improved knowledge mapping technology of power equipment defect text, which explores the practicality of this technology.

Citation Download Citation

Hang Zhang, Rui Yang, Kang Liu, Guangdong Zhang, and Jian Zhang "Power equipment defect intelligent diagnosis platform based on knowledge map", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127095Q (19 October 2023); https://doi.org/10.1117/12.2684642

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KEYWORDS

Associative arrays

Reliability

Instrument modeling

Transformers

Error analysis

Inspection equipment

Feature extraction

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