Design of experiments and machine learning to improve robustness of predictive maintenance with application to a real case study

DOI10.1080/03610918.2019.1656740OpenAlexW2971913361MaRDI QIDQ5082841

Riccardo Ceccato, Rosa Arboretti, Alberto Bianchi, Luigi Salmaso, Luca Pegoraro, Davide Scarabottolo, Silvio Restello

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1656740

zbMATH Keywords

machine learningpredictive maintenanceBig Datadesign of experiments (DOE)

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

A sequential designing-modeling technique when the input factors are not equally important

Cites Work

• Statistics in the big data era: failures of the machine
• When small data beats big data
• Principles of experimental design for big data analysis
• Approximation by superpositions of a sigmoidal function

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