The ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. Energy consumption sensing and collection enables the development of effective solutions to higher energy efficiency. Further, it is found that the data on energy consumption of manufacturing machines also contains the information on the conditions of these machines. In this paper, methods of machine anomaly detection based on energy consumption information are developed and applied to cases on our Syil X4 computer numerical control (CNC) milling machine. Further, given massive amount of energy consumption data from large amount machining tasks, the proposed algorithms are being implemented on a Storm and Hadoop based framework aiming at online realtime machine anomaly detection.
|Pages||136 - 142|
|Publication status||Published - Nov 2014|
|Event||International Conference on Advanced Cloud and Big Data (CBD) - Huangshan, China|
Duration: 20 Nov 2014 → 22 Nov 2014
|Conference||International Conference on Advanced Cloud and Big Data (CBD)|
|Period||20/11/14 → 22/11/14|
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EU Smarter project EU CASES project
- anomaly detection
- articifial neurak network