Energy Consumption Data Based Machine Anomaly Detection
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.
anomaly detection energy consumption manufacturing artificial neural network Hadoop Storm
Hui Chen Xiang Fei Sheng Wang Xin Lu Guoqin Jin Weidong Li Xuyang Wu
Department of Computing Coventry University Coventry,UK Department of Mechanical,Automotive and Manufacturing Engineering Coventry University Coventry,UK Department of Computer Science University College London London,UK
国际会议
2014 2nd International Conference on Advanced Cloud and Big Data (CBD 2014)(2014年先进云计算和大数据国际会议)
安徽黄山
英文
136-142
2014-11-20（万方平台首次上网日期，不代表论文的发表时间）