{"id":2120,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=2120"},"modified":"2021-07-13T18:58:22","modified_gmt":"2021-07-13T16:58:22","slug":"deviation-detection-in-production-processes-based-on-video-data-using-unsupervised-machine-learning-approaches","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/machine-tools-anomaly-detection\/deviation-detection-in-production-processes-based-on-video-data-using-unsupervised-machine-learning-approaches\/","title":{"rendered":"Deviation detection in production processes based on video data using unsupervised machine learning approaches"},"content":{"rendered":"\n

by Matthias Muehlbauer, Hendrik Epp, Hubert Wuerschinger, Nico Hanenkamp<\/em><\/em> (Germany)<\/em><\/p>\n\n\n\n

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

The detection of deviations within production processes is essential to ensure high productivity or avoid potential damage. Various approaches are available for this purpose. In the field of video surveillance, unsupervised machine learning methods have made significant progress in detecting deviations. In this paper, the transferability of these generic approaches to production processes is investigated. At first, an evaluation basis is created. Therefore, the variety of deviations, which can occur in an automated production process, is structured and covered as far as possible in video benchmark data sets. Subsequently, existing unsupervised approaches are selected, adapted and tested on the created data sets. In conclusion, the results show that the two chosen unsupervised autoencoder architectures can be partially used for generic deviation detection in the production domain. The main challenges identified are the large variety of different tasks and deviations in production processes. However, for further investigations, the development of even more detailed benchmark sets is essential.<\/p>\n\n\n\n

Keywords<\/strong>: Production process, Deviation detection, Anomaly detection, Unsupervised learning, Autoencoder, Computer vision, Video data<\/p>\n\n\n\n

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