Fault Diagnosis of Regenerative Water Heater Based-On Multi-class Support Vector Machines

Author

Wang, Lei ; Zhang, Rui-Qing

Author_Institution

Thermal Power Eng. Dept., Shenyang Inst. of Eng., Shenyang, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

489

Lastpage

492

Abstract

The main idea of multi-class support vector machines (SVMs) is described. a multi-class model for regenerative water heater fault diagnosis is presented combining the fuzzy logic and SVMs. The typical faults set of regenerative water heater is built after thoroughly analyzing the relationships between performance parameters and faults. Finally, the model is inspected and verified by an example in a regenerative water heater of the turbine unit, the result of diagnosis shows that it is simple and practical; it can identify the regenerative water heater faults effectively.

Keywords

fault diagnosis; fuzzy logic; power engineering computing; steam turbines; support vector machines; SVM; fault diagnosis; fuzzy logic; multiclass support vector machines; regenerative water heater; turbine unit; Artificial neural networks; Classification algorithms; Cogeneration; Constraint optimization; Fault diagnosis; Heat engines; Hydrogen; Support vector machines; Turbines; Water heating; fault diagnosis; fuzzy rules; regenerative water heater; steam turbine; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.431

Filename

5367052