DocumentCode
3381238
Title
Fault diagnosis method combining multi-relation indexes with D-S evidence theory
Author
Xiaojuan Han ; Xilin Zhang ; Fang Chen ; Zenan Chen
Author_Institution
Dept. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China
fYear
2011
fDate
15-16 Aug. 2011
Firstpage
90
Lastpage
93
Abstract
The fault diagnosis method based on grey relation analysis needs choosing reference pattern vectors which have a strongly ability of classify and identifying fault, otherwise the veracity and reliability of fault diagnosis can be greatly reduced. On basis of traditional grey relation analysis, multi-samples were adopted as reference signals and the relation indexes between multi-sample reference signals and the signal to be diagnosed are calculated by grey relation analysis method and normalized as the mass or basic probability assignment function which are fused to realize fault diagnosis in term of D-S evidence theory. The method provided in this paper is applied to the fault diagnosis of some reducer case operating state. The simulation result is shown that the reliability of fault diagnosis can be improved by fusion and the uncertainty of fault diagnosis depending on single reference pattern vector can be eliminated too.
Keywords
fault diagnosis; gears; grey systems; inference mechanisms; machine components; pattern classification; probability; reliability theory; signal processing; D-S evidence theory; fault classification; fault diagnosis method; fault identification; multirelation indexes; multisample reference signals; probability assignment function; reducer case operating state; reference pattern vectors; reliability; single reference pattern vector; traditional grey relation analysis; Accuracy; Educational institutions; Fault diagnosis; Indexes; Power transformers; Publishing; D-S theory; fault diagnosis; gear case; multi-relation index;
fLanguage
English
Publisher
ieee
Conference_Titel
Automation and Logistics (ICAL), 2011 IEEE International Conference on
Conference_Location
Chongqing
ISSN
2161-8151
Print_ISBN
978-1-4577-0301-0
Electronic_ISBN
2161-8151
Type
conf
DOI
10.1109/ICAL.2011.6024690
Filename
6024690
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