Details

Autor(en) / Beteiligte

• Meng-ting, Chen
• Xiao-wei, Fu
• Zhong-hua, Deng
• Xi, Li
• Xiao-long, Wu
• Yuan-wu, Xu
• Tao, Xue

Titel

Data-Driven Fault Detection for SOFC system based on Random Forest and SVM

Ist Teil von

• 2019 Chinese Automation Congress (CAC), 2019, p.2829-2834

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Accurately detecting faults is essential for the development and maintenance of safe and reliable Solid Oxide Fuel Cell (SOFC) system. In this paper, A fault detection framework combining data generation and data-driven modeling for SOFC system is proposed: The most important features are selected based on Random Forest and the Support Vector Machine with radial basis kernel function (rbf-SVM) is applied to realize multi-fault classification. The experimental results demonstrate that the method is feasible and effective, significantly improving the performance of fault detection and isolation (FDI).