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Appl. Sci. 2019, 9(8), 1549; https://doi.org/10.3390/app9081549

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

1
Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin 640, Taiwan
2
Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
3
Department of Management Information Systems, National Chung Hsing University, Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
Received: 22 February 2019 / Revised: 3 April 2019 / Accepted: 8 April 2019 / Published: 14 April 2019
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Abstract

Since the concept of Industry 4.0 emerged, an increasing number of major manufacturers have incorporated relevant technologies to monitor machinery and schedule processes so as to increase yield and optimize production. However, most machinery monitoring technologies are far too expensive for small- and medium-sized enterprises. Furthermore, the production processes at small- and medium-sized enterprises are simpler and can thus be optimized without excessively complex scheduling systems. This study therefore proposed the use of cheaper add-on sensors for monitoring machinery and integrated them with an algorithm that can more swiftly produce results that meet multiple objectives. The proposed algorithm is meant to extend the capabilities of small- and medium-sized enterprises in monitoring machinery and scheduling processes, thereby enabling them to contend on an equal footing with larger competitors. Finally, we performed an experiment at an actual spring enterprise to demonstrate the validity of the proposed algorithm. View Full-Text
Keywords: Industry 4.0; anomaly detection; scheduling; neural network; skyline queries Industry 4.0; anomaly detection; scheduling; neural network; skyline queries
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Chen, Y.-C.; Ting, K.-C.; Chen, Y.-M.; Yang, D.-L.; Chen, H.-M.; Ying, J. .-C. A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes. Appl. Sci. 2019, 9, 1549.

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