Fuzzy Logic Combined Logistic Regression Methodology for Gas Turbine First-Stage Nozzle Life Prediction
Significant aspects of intelligent maintenance include the abilities to diagnose impending failures, prognose the remaining useful lifetime of the process and schedule maintenance operations so that uptime is maximized. Prognosis is probably the most difficult of the three issues leading to total intelligent maintenance. This paper describes a fuzzy logic combined logistic regression method of fatigue severity assessment and remaining useful life prediction of gas turbine hot components. Logistic regression method is proposed to derive fuzzy logic rule base using historical maintenance running records and engineers’ experience. Implementation of the prognostic methodology presents a great opportunity to significantly enhance current engine health monitoring capabilities and risk management practices.
Kai Cheng, Yingxue Yao and Liang Zhou
J. H. Yan et al., "Fuzzy Logic Combined Logistic Regression Methodology for Gas Turbine First-Stage Nozzle Life Prediction", Applied Mechanics and Materials, Vols. 10-12, pp. 583-587, 2008