International Journal of Applied Science and Engineering

Published by Chaoyang University of Technology

Li-Hua Li, Chang-Yu Lai*, Fu-Hsiang Kuo and Pei-Yu Chai

Department of Information Management, Chaoyang University of Technology, Taichung City, Taiwan


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Predictive maintenance is one of the key subjects for Industrial 4.0. The purpose of predictive maintenance is to reduce unplanned downtime, to increase productivity and to reduce production costs. In the repetitive procedures of manufacturing & production, raw materials are put into or picked out from storage warehouse and in some cases are replaced with labour-intensive operations using machineries and equipment. The high-efficiency motor is the core of the automatic storage warehouse. The personnel who supervise the equipment need to check if a machine has any malfunction? Any downtime for maintenance is the waste of production time.
This research integrates the ANN model of machine learning (ML) to build an intelligent predictive maintenance system for the motor of vertical lift storage. Results of the proposed method are presented which show that our method has the ability to reduce waste, costs, and thus improve efficiency of supply chain.

Keywords: Predictive maintenance system; industrial 4.0; machine learning; artificial neural network (ANN).

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Received: 2019-05-23
Revised: 2019-06-21
Accepted: 2019-08-14
Publication Date: 2019-09-01

Cite this article:

Li, L.H., Lai, C.Y., Kuo, F.H., Chai, P.Y. 2019. Predictive maintenance of vertical lift storage motor based on machine learning. International Journal of Applied Science and Engineering, 16, 109-118.

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