Health risk assessment and prognosis of gas turbine blades by simulation and statistical methods

Algorithmic approaches for failure risk assessment, anomaly detection and life prognosis of gas turbine blade are discussed. Modeling of blade tip clearance and Monte Carlo simulation considering creep, vibration and other damaging effects lead to two probabilistic distributions with blade tip clearance data. Failure risk can be determined during blade life usage based on blade tip tolerance limits. Statistical treatments considering percentile ranking of sample mean and regression analysis of blade tip clearance data for anomaly detection and usage life analysis respectively are also discussed.