NDLI: Alarm prediction in industrial machines using autoregressive LS-SVM models

Content Provider	IEEE Xplore Digital Library
Author	Langone, R. Alzate, C. Bey-Temsamani, A. Suykens, J.A.K.
Copyright Year	2014
Description	Author affiliation: Flanders Mechatron. Technol. Centre (FMTC vzw), Leuven, Belgium (Bey-Temsamani, A.) \|\| Smarter Cities Technol. Center, IBM Res. - Ireland, Dublin, Ireland (Alzate, C.) \|\| Dept. of Electr. Eng. (ESAT), KU Leuven, Leuven, Belgium (Langone, R.; Suykens, J.A.K.)
Abstract	In industrial machines different alarms are embedded in machines controllers. They make use of sensors and machine states to indicate to end-users various information (e.g. diagnostics or need of maintenance) or to put machines in a specific mode (e.g. shut-down when thermal protection is activated). More specifically, the alarms are often triggered based on comparing sensors data to a threshold defined in the controllers software. In batch production machines, triggering an alarm (e.g. thermal protection) in the middle of a batch production is crucial for the quality of the produced batch and results into a high production loss. This situation can be avoided if the settings of the production machine (e.g. production speed) is adjusted accordingly based on the temperature monitoring. Therefore, predicting a temperature alarm and adjusting the production speed to avoid triggering the alarm seems logical. In this paper we show the effectiveness of Least Squares Support Vector Machines (LS-SVMs) in predicting the evolution of the temperature in a steel production machine and, as a consequence, possible alarms due to overheating. Firstly, in an offline fashion, we develop a nonlinear autoregressive (NAR) model, where a systematic model selection procedure allows to carefully tune the model parameters. Afterwards, the NAR model is used online to forecast the future temperature trend. Finally, a classifier which uses as input the outcomes of the NAR model allows to foresee future alarms.
Starting Page	359
Ending Page	364
File Size	960832
Page Count	6
File Format	PDF
ISBN	9781479945184
DOI	10.1109/CIDM.2014.7008690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature measurement Temperature sensors Production Predictive models Steel Mathematical model
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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