A Fault Discrimination Reasoning Method Based on Knowledge Graph

Abstract

The present invention proposes a fault discrimination and reasoning method based on knowledge graph, which acquires equipment data, fault data, and disposal plan data; establishes an equipment map based on equipment data, establishes a fault map based on fault data, and establishes a disposal plan map based on event data; The extraction algorithm and the TranSE algorithm perform map fusion, map completion, and map reasoning on the equipment map, fault map, and disposal plan map to obtain a knowledge map; use the graph neural network to perform fault discrimination and reasoning on the knowledge map; use a large amount of equipment data and historical faults The data and the treatment plan data are respectively established atlases, and then the atlases are fused and completed, and the final knowledge atlas is used to perform fault discrimination and reasoning, and automatically provide methods for discrimination, diagnosis, and maintenance for transformer faults. The method is accurate in diagnosing faults Good performance, timely and reliable.

Description

Technical field [0001] The present invention is in the field of fault inference, particularly to a knowledge-based failure determination map reasoning. Background technique [0002] Troubleshooting current transformer rely mainly on-site professionals, and even the need for a specific expert, when transformer failure, it may not be timely maintenance, resulting in unpredictable results, and high maintenance costs. Given the problem of artificial investigation transformer fault, but also a professional research systems, such as expert systems, maintenance personnel can quickly help find cause of the malfunction and breakdown maintenance program. But the higher cost of building expert systems, general accuracy, high maintenance costs, can not adapt to the new situation, there is cause actual positioning reasons, maintenance suggestions given case of an error. Inventive content [0003] In response to these problems, the present invention provides a method for determining reasoning...

AI Technical Summary

Problems solved by technology

However, the construction cost of the expert system is high, the accuracy is average, the maintenance cost is high, and it cannot adapt to the new situation, resulting in the fact that the actual use has positioning reasons and the maintenance suggestions given are wrong.

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