{"title":"Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0","user_id":"83781","page":"87-92","department":[{"_id":"DEP5000"}],"publication":" Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1","conference":{"start_date":"2020-11-02","end_date":"2020-11-04","location":"Budapest, HUNGARY","name":"International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL)"},"date_created":"2025-04-17T06:20:07Z","year":"2020","date_updated":"2025-04-17T06:26:58Z","_id":"12812","abstract":[{"lang":"eng","text":"Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data."}],"doi":"10.5220/0010130300870092","status":"public","type":"conference_editor_article","editor":[{"first_name":"H.","last_name":"Panetto","full_name":"Panetto, H."},{"full_name":"Madani, K.","last_name":"Madani","first_name":"K."},{"full_name":"Smirnov, A.","last_name":"Smirnov","first_name":"A."}],"publication_identifier":{"isbn":["978-989-758-476-3"]},"citation":{"mla":"Berns, Fabian, et al. “Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0.” <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>, edited by H. Panetto et al., SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92, <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>.","chicago":"Berns, Fabian, Markus Lange-Hegermann, and Christian Beecks. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. Edited by H. Panetto, K. Madani, and A. Smirnov. <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications, 2020. <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>.","chicago-de":"Berns, Fabian, Markus Lange-Hegermann und Christian Beecks. 2020. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. Hg. von H. Panetto, K. Madani, und A. Smirnov. <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications. doi:<a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Berns, Fabian</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Beecks, Christian</span> ; <span style=\"font-variant:small-caps;\">Panetto, H.</span> ; <span style=\"font-variant:small-caps;\">Madani, K.</span> ; <span style=\"font-variant:small-caps;\">Smirnov, A.</span> (Hrsg.): <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i> : SCITEPRESS - Science and Technology Publications, 2020","havard":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020.","van":"Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. Panetto H, Madani K, Smirnov A, editors.  Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications; 2020.","bjps":"<b>Berns F, Lange-Hegermann M and Beecks C</b> (2020) <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>, Panetto H, Madani K and Smirnov A (eds). SCITEPRESS - Science and Technology Publications.","ufg":"<b>Berns, Fabian/Lange-Hegermann, Markus/Beecks, Christian</b>: Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, hg. von Panetto, H./Madani, K./Smirnov, A., o. O. 2020.","ieee":"F. Berns, M. Lange-Hegermann, and C. Beecks, <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92. doi: <a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>.","short":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020.","ama":"Berns F, Lange-Hegermann M, Beecks C. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. (Panetto H, Madani K, Smirnov A, eds.). SCITEPRESS - Science and Technology Publications; 2020:87-92. doi:<a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>","apa":"Berns, F., Lange-Hegermann, M., &#38; Beecks, C. (2020). Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto, K. Madani, &#38; A. Smirnov (Eds.), <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i> (pp. 87–92). SCITEPRESS - Science and Technology Publications. <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>"},"external_id":{"isi":["WOS:000799452300009"]},"language":[{"iso":"eng"}],"keyword":["Anomaly Detection","Gaussian Processes","Explainable Machine Learning","Industry 4.0"],"publisher":"SCITEPRESS - Science and Technology Publications","author":[{"first_name":"Fabian","last_name":"Berns","full_name":"Berns, Fabian"},{"id":"71761","full_name":"Lange-Hegermann, Markus","last_name":"Lange-Hegermann","first_name":"Markus"},{"first_name":"Christian","full_name":"Beecks, Christian","last_name":"Beecks"}],"publication_status":"published"}