Title :
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
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;
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
DOI :
10.1109/ICICTA.2010.832
