Energy Consumption Data Based Machine Anomaly Detection

Hui Chen, Xiang Fei, Sheng Wang, Xin Lu, Guoqin Jin, Weidong Li, X Wu

    Research output: Contribution to conferencePaper

    10 Citations (Scopus)
    86 Downloads (Pure)


    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.
    Original languageEnglish
    Pages136 - 142
    Publication statusPublished - Nov 2014
    EventInternational Conference on Advanced Cloud and Big Data (CBD) - Huangshan, China
    Duration: 20 Nov 201422 Nov 2014


    ConferenceInternational Conference on Advanced Cloud and Big Data (CBD)

    Bibliographical note

    © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    EU Smarter project EU CASES project


    • anomaly detection
    • articifial neurak network
    • Hadoopm
    • Storm


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