Dynamic Weighted Fusion of Multi-source Information for Large Rotating Machinery Fault Prediction

Author

Jianghong, Sun ; Yunbo, Zuo ; Xiaoli, Xu

Author_Institution

Sch. of Electromech. Eng., Beijing Inf. Sci. & Technol. Univ., Beijing, China

Volume

2

fYear

2010

fDate

11-12 May 2010

Firstpage

173

Lastpage

176

Abstract

Data process of large rotating machinery is in line with basic features of information fusion. The fault deterioration is extracted from the pattern spectrum as the fault feature, and its trend is predicted by the information fusion which is based on the dynamic weighted method for single-sensor and multi-sensors respectively. Actual example of Beijing Yanshan Petrochemical Co. shows the correction of conclusion.

Keywords

fault diagnosis; machinery; mechanical engineering computing; sensor fusion; Beijing Yanshan Petrochemical Co; dynamic weighted fusion; multisensors; multisource information; rotating machinery fault prediction; Data mining; Fault diagnosis; Feature extraction; Information analysis; Intelligent vehicles; Machine intelligence; Machinery; Petrochemicals; Sensor phenomena and characterization; Vehicle dynamics; dynamic weighted method; fault deterioration; information fusion; large rotating machinery;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-7279-6

Electronic_ISBN

978-1-4244-7280-2

Type

conf

DOI

10.1109/ICICTA.2010.832

Filename

5523187