A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0

  • To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.

Export metadata

Additional Services

Share in X Search Google Scholar
Metadaten
Author:Hamid Khayyam, Ali Jamali, Alireza Bab-Hadiashar, Thomas EschORCiD, Seeram Ramakrishna, Mahdi Jalil, Minoo Naebe
DOI:https://doi.org/10.1109/ACCESS.2020.2999898
ISBN:2169-3536
Parent Title (English):IEEE Access
Publisher:IEEE
Place of publication:New York, NY
Document Type:Article
Language:English
Year of Completion:2020
Date of the Publication (Server):2020/06/22
First Page:1
Last Page:12
Link:https://doi.org/10.1109/ACCESS.2020.2999898
Zugriffsart:weltweit
Institutes:FH Aachen / ECSM European Center for Sustainable Mobility
FH Aachen / Fachbereich Luft- und Raumfahrttechnik
open_access (DINI-Set):open_access
collections:Verlag / IEEE
Open Access / Gold
Licence (German): Creative Commons - Namensnennung