{"id":28501,"date":"2023-09-25T12:55:24","date_gmt":"2023-09-25T10:55:24","guid":{"rendered":"https:\/\/bi-rex.it\/portfolio-items\/knowledge-integration-in-neural-networks-for-e-maintenance-kinema\/"},"modified":"2023-10-10T09:55:00","modified_gmt":"2023-10-10T07:55:00","slug":"knowledge-integration-in-neural-networks-for-e-maintenance-kinema","status":"publish","type":"avada_portfolio","link":"https:\/\/bi-rex.it\/en\/portfolio-items\/knowledge-integration-in-neural-networks-for-e-maintenance-kinema\/","title":{"rendered":"Knowledge Integration in Neural networks for e-Maintenance – KINEMA"},"content":{"rendered":"

KINEMA<\/h1><\/div>

Knowledge Integration in Neural networks for e-Maintenance<\/h2>\n<\/div>

<\/i><\/i><\/span>Nature of Project and Objectives<\/span><\/a><\/h3><\/div>
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The use of data-driven methods offers great potential in terms of improving the maintenance policies of mechanical systems. However, these methods are not without limitations and are often applied without adequately exploiting the knowledge available in the field. KINEMA aims to use state-of-the-art Machine Learning<\/strong> methods in an innovative way, in order to maximise the exploitation of available knowledge across the entire production chain for maintenance purposes<\/strong>. The objective will be achieved by defining an integration methodology for heterogeneous models within a neural architecture. The approach will facilitate the exploitation of existing knowledge, the reuse of predictive models, and the reduction of the amount of data needed for model training. The approach will be demonstrated through the development of operational diagnostic systems on three industrial use cases<\/strong>.<\/p>\n<\/div><\/div><\/div>

<\/i><\/i><\/span>Role played by BI-REX<\/span><\/a><\/h3><\/div>
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