Title: Machine learning algorithms benchmarking for real-time fault predictable scheduling on a shop floor

Authors: Wenda Wu; Wei Ji; Lihui Wang; Liang Gao

Addresses: Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden ' Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China ' Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden ' State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430073, China

Abstract: To select a proper machine learning algorithm for fault predictable scheduling on a shop floor, ten algorithms in the machine learning field have been selected, implemented and compared in this research. Due to the lack of applicable real data to the authors, a data generation method is proposed in terms of data complexity, number of data attributes and data depth. On top of the method, six datasets are generated by selecting three-level data attributes and three-level data depths, which were used to train the ten algorithms. The performances of the algorithms are evaluated by considering three indexes including, training accuracy, testing time and training time. The results demonstrate that naive Bayes classifier is suitable to low-complexity data and that convolutional neural network and deep belief network fit well in high-complexity data, such as the real data. [Submitted 25 June 2018; Accepted 27 May 2019]

Keywords: machine learning; benchmarking; fault prediction; scheduling.

DOI: 10.1504/IJMR.2021.114006

International Journal of Manufacturing Research, 2021 Vol.16 No.1, pp.1 - 20

Received: 25 Jun 2018
Accepted: 27 May 2019

Published online: 16 Mar 2021 *

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