Power-signature-based Bayesian multi-classifier for operation mode identification
| Autor: | Hian-Leng Chan, Omid Geramifard, Xiang Li, Zhao Yi Zhi, Chua Yong Quan |
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| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Engineering business.industry Bayesian probability 02 engineering and technology Energy consumption computer.software_genre Potential energy 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Process optimization Data mining Electric power business computer Classifier (UML) Injection molding machine Efficient energy use |
| Zdroj: | ETFA |
| DOI: | 10.1109/etfa.2016.7733530 |
| Popis: | In this paper, a power-signature-based Bayesian multi-classifier is proposed to identify various operational modes of a complex machinery system that can help determine the energy contribution of different operation modes, identify potential energy hot-spots and provide basis for more accurate energy consumption calculation. This technology can also help process experts and managers to perform the process optimization from an energy saving point of view, and benchmark the energy efficiency of the processes. Based on our experimental results on an Engel injection molding machine, our proposed approach can successfully classify its operation modes to an acceptable extent based on its electrical power signatures. |
| Databáze: | OpenAIRE |
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