Condenser Fault Diagnosis Based on FNN and Data Fusion

Abstract:

In order to improve the fault diagnosis result of the condenser, one new approach based on the fuzzy neural network and data fusion is proposed in this paper. Firstly, the data from the various sensors can be processed through the specific membership functions. With the fault symptoms and fault types of condenser, the fuzzy neural network is constructed for the primary fault diagnosis. Some likelihood of the neural network outputs is too close to make the correct decision of fault diagnosis. The problem can be solved by the data fusion technology. This method was successfully adopted in the application of condenser fault diagnosis. Compared with the general method of FNN, this approach can enhance the accuracy in the domain of fault diagnosis, especially for reducing the uncertainty in the fault diagnosis.

• Periodical: Applied Mechanics and Materials (Volumes 44-47)
• Pages: 3762-3766
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.3762
• Online since: December 2010
• Authors: Fei Xia, Hao Zhang, Dao Gang Peng, Hui Li, Yi Kang Su
• Keywords: Condenser, Data Fusion, Fault Diagnosis, Fuzzy Neural Network (FNN)

References

[1] E. Zhang, H. Zhang and B. Xue: Control and Automation, vol. 23(2007), pp.192-194.

[2] X. Jia: Fault Diagnosis in Turbine of Condenser(Harbin Engineering University, Haerbin, 2004).

[3] L. Yang and H. Yu: Theory and Application of Multi-Source Information Fusion(Beijing University of Posts and Telecommunications Press, Beijing, 2005).

[4] G. Niu, T. Han and B.S. Yang: Mechanical Systems and Signal Processing, vol. 21(2007), pp.1285-1299.

[5] B.S. Yang and K.J. Kim: Mechanical Systems and Signal Processing vol. 20 (2006), pp.403-420.

[6] Pu Shujin, Yang Shenyuan, Hu Weiwei: The 6th World Congress on Intelligent Control and Automation, 2006, 1: 4264-4268.

DOI: 10.1109/wcica.2006.1713179