Intelligent Electromagnetic Compatibility Diagnosis and Management With Collective Knowledge Graphs and Machine Learning

Abstract

The explosive growth of electronic devices brings a soaring demand for rapid electromagnetic compatibility (EMC) diagnosis. However, there is a significant learning curve for the electrical engineers to apply EMC knowledge. In this article, an efficient EMC diagnosis and management methodology was proposed, which provided a fast way for EMC analysis in seconds other than traditional simulation or calculation. The approach organized the EMC knowledges as knowledge graph composed by the interference/sensitive units, and mathematical set rules. The optimized graph structure is in form of rule, maxterm, basic unit, and entity layers. Based on the condensed relationships, it achieved high searching efficiency and graph expansibility. To facilitate the information retrieval from the knowledge graph, the interference/sensitive units and related parameters were acquired from interactive sessions, in which long short-term memory method was used to extract entities. The EMC specialized corpora were fed in training to enhance the accuracy of inference. Finally, the EMC diagnosis and management reports were automatically generated by knowledge graph searching application. The proposed method improved the calculation efficiency by three times. The storage of relationships and attributes of nodes was reduced by 76% and 60.7%. The identification accuracy was enhanced from 77.7% to 99.5%. The presented method is practically useful for EMC design in crosstalk analysis, radiated, and conducted interference diagnoses.