Fault Diagnosis with Fuzzy Expert System

Yang Lan Ou

doi:10.4028/www.scientific.net/AMM.48-49.519

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 48-49Fault Diagnosis with Fuzzy Expert System

Fault Diagnosis with Fuzzy Expert System

Abstract:

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared with the expected values of the same quantities that are calculated using numerical models of local subsystems. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge based on fuzzy expert system.

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Periodical:

Applied Mechanics and Materials (Volumes 48-49)

Pages:

519-522

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.48-49.519

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Online since:

February 2011

Authors:

Yang Lan Ou

Keywords:

Deviation, Fault Diagnosis, Fault Mode, Fuzzy Expert System

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References

[1] Marinai, L., Probert, D., and Singh, R., 2004, Prospects for Aero Gas-Turbine Diagnostics: A Review, Appl. Energy, 79, p.109–126.

DOI: 10.1016/j.apenergy.2003.10.005

Google Scholar

[2] Biagetti, T., and Sciubba, E., 2004, Automatic Diagnostics and Prognostics of Energy Conversion Processes Via Knowledge-Based Systems, Energy, 29, p.2553–2572.

DOI: 10.1016/j.energy.2004.03.031

Google Scholar

[3] Ogaji, S. O. T., Marinai, L., Sampath, S., Singh, R., and Probert, S. D., 2005, Gas-Turbine Fault Diagnostics: A Fuzzy-Logic Approach, Appl. Energy, 82, p.81–89.

DOI: 10.1016/j.apenergy.2004.07.004

Google Scholar

[4] Verma, R., Roy, N., and Ganguli, R., 2006, Gas Turbine Diagnostics Using a Soft Computing Approach, Appl. Math. Comput., 172, p.1342–1363.

DOI: 10.1016/j.amc.2005.02.057

Google Scholar

[5] Pawar, P. M., and Ganguli, R., 2007, Fuzzy-Logic-Based Health Monitoring and Residual-Life Prediction for Composite Helicopter Rotor, J. Aircr., 44, p.981–995.

DOI: 10.2514/1.26495

Google Scholar

[6] Martis, D., 2007, Fuzzy Logic Estimation Applied to Newton Methods for Gas Turbines, ASME J. Eng. Gas Turbines Power, 129, p.88–96.

DOI: 10.1115/1.2360597

Google Scholar

[7] Toffolo, A., and Lazzaretto, A., 2008, Energy System Diagnosis by a Fuzzy Expert System With Genetically Evolved Rules, Int. J. Thermodyn., 11, p.115–121.

Google Scholar