A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling With Application in Industry 4.0

IEEE Access, vol. 8, pp. 111381–111393

Abstract

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.

Authors 7

1. Hamid Khayyam

   RMIT University

   Affiliation as printed

   School of Engineering, RMIT University, Melbourne, Australia

   ORCiD

   School of Engineering, RMIT University, Melbourne, VIC, Australia

2. Ali Jamali

   University of Guilan

   Affiliation as printed

   Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran

3. Alireza Bab‐Hadiashar

   RMIT University

   Affiliation as printed

   School of Engineering, RMIT University, Melbourne, Australia

   School of Engineering, RMIT University, Melbourne, VIC, Australia

4. Thomas Esch Aachen Mechanical Engineering

   FH Aachen

   Affiliation as printed

   Mechanical Engineering, FH Aachen University of Applied Sciences, Aachen, Germany

5. Seeram Ramakrishna

   National University of Singapore

   Affiliation as printed

   Department of Mechanical Engineering, National University of Singapore, Singapore

6. Mahdi Jalili

   RMIT University

   Affiliation as printed

   School of Engineering, RMIT University, Melbourne, Australia

   ORCiD

   School of Engineering, RMIT University, Melbourne, VIC, Australia

7. Minoo Naebe

   Deakin University

   Affiliation as printed

   Institute for Frontier Materials, Deakin University, Burwood, Australia

   Institute for Frontier Materials, Deakin University, Burwood, VIC, Australia