Matteo Barbieri, PhD
Matteo Barbieri, PhD
Former post-doc of Department of Electrical, Electronic, and Information Engineering "Guglielmo
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RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques
M Barbieri, KTP Nguyen, R Diversi, K Medjaher, A Tilli
Journal of Intelligent Manufacturing 32, 1421-1440, 2021
Condition monitoring of ball bearings using estimated AR models as logistic regression features
M Barbieri, R Diversi, A Tilli
2019 18th European Control Conference (ECC), 3904-3909, 2019
An Onboard Model‐of‐signals Approach for Condition Monitoring in Automatic Machines
M Barbieri, A Bosso, C Conficoni, R Diversi, M Sartini, A Tilli
Enterprise Interoperability: Smart Services and Business Impact of …, 2018
Condition monitoring by model-of-signals: Application to gearbox lubrication
M Barbieri, F Mambelli, R Diversi, A Tilli, M Sartini
15th European Workshop on Advanced Control and Diagnosis, 629-642, 2018
Condition monitoring of electric-cam mechanisms based on Model-of-Signals of the drive current higher-order differences
M Barbieri, R Diversi, A Tilli
IFAC-PapersOnLine 53 (2), 802-807, 2020
Recursive identification of errors-in-variables models with correlated output noise
M Barbieri, R Diversi
IFAC-PapersOnLine 54 (7), 363-368, 2021
Identification of noisy input-output FIR models with colored output noise
M Barbieri, R Diversi
IFAC-PapersOnLine 53 (2), 901-906, 2020
Condition Monitoring of a Paper Feeding Mechanism Using Model-of-Signals as Machine Learning Features
M Barbieri, F Mambelli, J Lucchi, R Diversi, A Tilli, M Sartini
PHM Society European Conference 5 (1), 10-10, 2020
Robust Global Asymptotic Stabilization of Linear Cascaded Systems With Hysteretic Interconnection
A Bosso, L Zaccarian, A Tilli, M Barbieri
IEEE Control Systems Letters 7, 337-342, 2022
Advanced Condition Monitoring of Complex Mechatronics Systems Based on Model-of-Signals and Machine Learning Techniques
M Barbieri
alma, 2021
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