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Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

1
Department of Industrial and Manufacturing System Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA 22904 USA
*
Author to whom correspondence should be addressed.
Energies 2019, 12(11), 2216; https://doi.org/10.3390/en12112216
Received: 6 May 2019 / Revised: 5 June 2019 / Accepted: 10 June 2019 / Published: 11 June 2019
(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems )
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Abstract

The energy efficient operation of a manufacturing system is important for sustainable development of industry. Apart from the device and process level, energy saving methods at the system level has attracted increasing attention with the rapid growth of the industrial Internet of things technology, which makes it possible to sense and collect real-time data from the production line and provide more opportunities for online control for energy saving purposes. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to decide the machine energy saving state considering the production information of a discrete stochastic manufacturing system. Fuzzy knowledge for energy saving operations of a machine is represented in weighted fuzzy production rules with certain values. The rules describe uncertain, imprecise, and ambiguous knowledge of machine state decisions. This makes an energy saving sleep decision in advance when a machine has the inclination of starvation or blockage, which is based on the real-time production rates and level of connected buffers. A dynamic adaptive fuzzy reasoning Petri net is formally defined to implement the reasoning process of the machine state decision. A manufacturing system case is used to demonstrate the application of our method and the results indicate its effectiveness for energy saving operation purposes. View Full-Text
Keywords: knowledge representation; fuzzy reasoning Petri net; energy efficient operation; manufacturing system knowledge representation; fuzzy reasoning Petri net; energy efficient operation; manufacturing system
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Wang, J.; Fei, Z.; Chang, Q.; Li, S. Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net. Energies 2019, 12, 2216.

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