{"id":2905,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=2905"},"modified":"2022-07-26T13:03:17","modified_gmt":"2022-07-26T11:03:17","slug":"introduction-to-deep-degradation-metric-in-smart-production-ecosystems","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/biological-transformation-sustainability-human-factors\/introduction-to-deep-degradation-metric-in-smart-production-ecosystems\/","title":{"rendered":"Introduction to deep degradation metric in smart production ecosystems"},"content":{"rendered":"\n

by Yeremia Gunawan Adhisantoso, Quy Le Xuan, Christoph Kellerman, Marco Munderloh, J\u00f6rn Ostermann (Germany)<\/em><\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

With the advent of Industry 4.0, more data is exploited to improve efficiency in production and to enable a cost-effective maintenance approach called predictive maintenance. In a production ecosystem, assets are maintained based on their corresponding internal condition. Often, there is no known ground truth or label for the internal condition of the components, especially for high-dimensional data. Furthermore, the initial degradation condition of the assets differs from each other. We present a novel approach to learns a degradation metric implicitly (with minimal information). The approach can take the initial condition of the asset into consideration.<\/p>\n\n\n\n

Keywords<\/strong>: Computer vision; Predictive maintenance; Prognostic health management<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n