Title :
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
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;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.431
