Abstract
Management of electromagnetic compatibility (EMC) is a key aspect of product manufacturing. The fierce industry competition and numerous regulations (guidelines) make EMC architects, engineers, and manufacturers increasingly anxious to apply EMC management in each phase (design, validation, development, production, deployment, and monitoring) by depending mainly on their experience. To organize the knowledge, regulations, and implementations of EMC management, this study introduces a new knowledge base with a collection of entities, their properties, and a rich set of relationships among them. Then, a machine learning-based framework for EMC management automation and intelligence is proposed. Convolutional neural network is applied to establish and classify the entities, and the knowledge-based question-answering approach is used for question answering. Finally, the EMC management plan is generated automatically. Two experiments were designed to evaluate the proposed method, and results show that both good recall and precision are achieved in the comprehensive EMC management scenarios. This method is useful for the practical EMC management of industrial products and can enhance product quality, shorten the production cycle, and reduce production cost.
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