Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/14155
Title: Energy anomaly detection in tire curing by using data integration and forecasting techniques
Authors: Yang, Hai-dong
Guo, Jian-hua
Liu, Guo-sheng
Keywords: Artificial neural network (ANN)
Data integration
Energy consumption anomaly
Energy saving
Support vector machine (SVM)
Tire curing
Issue Date: Jun-2012
Publisher: NISCAIR-CSIR, India
Abstract: This study proposed a method of energy anomaly detection by using data integration and forecasting techniques to improve energy efficiency in tire curing. Proposed method integrates energy consumption with different factors (environments, equipments, operators, tire blanks and tire types). Artificial neural network model and Support Vector Machine model were used to forecast normal interval for energy efficiency ratio; instances dropping out of this interval indicate potential anomaly affairs. Compared with traditional method, proposed method is robust against environment changes, highly correlated to curing process and can discover curing energy anomalies (leakage of steam or nitrogen, idling, and improper curing parameters configuration) effectively.
Description: 385-391
URI: http://hdl.handle.net/123456789/14155
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.71(06) [June 2012]

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