Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions P Baraldi, F Cannarile, F Di Maio, E Zio Engineering Applications of Artificial Intelligence 56, 1-13, 2016 | 140 | 2016 |
An evidential similarity-based regression method for the prediction of equipment remaining useful life in presence of incomplete degradation trajectories F Cannarile, P Baraldi, E Zio Fuzzy Sets and Systems 367, 36-50, 2019 | 27 | 2019 |
A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes F Cannarile, M Compare, E Rossi, E Zio Annals of Nuclear Energy 110, 739-752, 2017 | 19 | 2017 |
An unsupervised clustering method for assessing the degradation state of cutting tools used in the packaging industry F Cannarile, P Baraldi, M Compare, D Borghi, L Capelli, M Cocconcelli, ... Safety and Reliability. Theory and Applications, 138-139, 2017 | 16 | 2017 |
Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems F Cannarile, M Compare, P Baraldi, G Diodati, V Quaranta, E Zio Aerospace Science and Technology 94, 105392, 2019 | 13 | 2019 |
Homogeneous continuous-time, finite-state, hidden semi-Markov modelling for enhancing empirical classification system diagnostics of industrial components F Cannarile, M Compare, P Baraldi, F Di Maio, E Zio Machines 6 (3), 2018 | 12 | 2018 |
A clustering approach for mining reliability big data for asset management F Cannarile, M Compare, F Di Maio, E Zio Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2018 | 12 | 2018 |
Handling reliability big data: A similarity-based approach for clustering a large fleet of assets F Cannarile, M Compare, F Di Maio, E Zio Safety and Reliability of Complex Engineered Systems: ESREL 2015, 891-896, 2015 | 10 | 2015 |
A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms F Cannarile, P Baraldi, P Colombo, E Zio International Journal of Prognostics and Health Management 9 (1), 2018 | 6 | 2018 |
Comparison of Weibayes and Markov Chain Monte Carlo methods for the reliability analysis of turbine nozzle components with right censored data only F Cannarile, M Compare, S Mattafirri, F Carlevaro, E Zio Safety and Reliability of Complex Engineered Systems: ESREL 2015, 1937-1944, 2015 | 5 | 2015 |
A fault diagnostic tool based on a first principle model simulator F Cannarile, M Compare, E Zio Model-Based Safety and Assessment: 5th International Symposium, IMBSA 2017 …, 2017 | 4 | 2017 |
A Framework for the Application of AI Solutions for Facilitating and Speeding-Up the Industrialization of Innovative R&D Technologies for Targeting Net-Zero Emissions M Suardi, F Cannarile, G Guastone, A Fidanzi, R Millini, D Testa Abu Dhabi International Petroleum Exhibition and Conference, D011S008R006, 2023 | 1 | 2023 |
A Business-Oriented Feature Selection Method for Enhancing Machine Learning Based Digital Tools Adoption in the Energy Industry A Corneo, M Suardi, L Lancia, F Cannarile, A Fidanzi ADIPEC, 2022 | 1 | 2022 |
A Change Point Detection Approach for Intelligent Real-Time Identification of Lost Circulation Events During Drilling Operations F Cannarile, S Montoli, G Giliberto, M Suardi, B Di Bari, G Formato, ... Abu Dhabi International Petroleum Exhibition & Conference, 2021 | 1 | 2021 |
Anomaly Detection for Industrial Systems using Generative Adversarial Networks M Xu, P Baraldi, X Lu, F Cannarile, E Zio Proceedings of the 4th International Conference on System Reliability and …, 2019 | 1 | 2019 |
A heterogeneous ensemble approach for the prediction of the remaining useful life of packaging industry machinery F Cannarile, P Baraldi, M Compare, D Borghi, L Capelli, E Zio | 1* | |
DisruptionBench: A robust benchmarking framework for machine learning-driven disruption prediction S Lucas, M Bonotto, W Arnold, D Chayapathy, T Gallingani, A Spangher, ... | | 2024 |
A device-independent pipeline for benchmarking AI-driven disruption prediction models F Cannarile, M Bonotto, RS Pinna, A Fidanzi, M Parisi, F Zanon, ... Workshop on Artificial Intelligence for Accelerating Fusion and Plasma Science, 2023 | | 2023 |
The Aramis Data Challenge to prognostics and health management methods for application in evolving environments P Baraldi, M Compare, E Zio, F Cannarile, Z Yang Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2023 | | 2023 |
Do Fusion Plasma Time-Series Have a Persistent Memory that Machine Learning May Exploit? L Spangher, J Zhu, C Rea, A Spangher, W Arnold, M Bonotto, F Cannarile, ... APS Division of Plasma Physics Meeting Abstracts 2023, JP11. 121, 2023 | | 2023 |