Remaining Useful Life Estimation with Lamb-Wave Sensors Based on Wiener Process and Principal Components Regression

This paper proposes a new approach for predicting the remaining useful life of a structure from Lamb wave sensor data using principal component regression and Wiener process degradation modeling. Principal component regression is used for extracting damage sensitive features of a Lamb wave sensor signal and establish a relation between the features and measured areas. A Wiener process is developed to model the random delamination growth and predict future damage and failure time probability. We use minimum squared prediction error to find the optimum number of features and the window inside a Lamb wave signal that is most sensitive to the fatigue damage. Method is illustrated on real sensor data collected under run-to-failure fatigue loading conditions.