Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data
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- Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
- Helene Dernis & Petros Gkotsis & Nicola Grassano & Shohei Nakazato & Mariagrazia Squicciarini & Brigitte van Beuzekom & Antonio Vezzani, 2019. "World Corporate Top R&D investors: Shaping the Future of Technologies and of AI," JRC Research Reports JRC117068, Joint Research Centre.
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- Sareer Ul Amin & Mohib Ullah & Muhammad Sajjad & Faouzi Alaya Cheikh & Mohammad Hijji & Abdulrahman Hijji & Khan Muhammad, 2022. "EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos," Mathematics, MDPI, vol. 10(9), pages 1-15, May.
- Lukas-Valentin Herm & Theresa Steinbach & Jonas Wanner & Christian Janiesch, 2022. "A nascent design theory for explainable intelligent systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2185-2205, December.
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Keywords
anomaly detection; bayesian methods; black-box models; CUSUM method; data analytics; explainable artificial intelligence; machine learning; prognostic and health management; singular value decomposition;All these keywords.
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