  Non-destructive testing on metallic turbine blades is a challenging task due
to their complex geometry. Vibrational test-ing such as Process Compensated
Resonance Testing (PCRT) has shown an efficient approach, which first measures
the vibrational response of the turbine blades, and then employs a classifier
to determine if the quality of the turbine blades are good or bad. Our previous
work mainly concentrated on the development of Mahalanobis distance-based
classifiers which are fed by the measured vibrational features (such as
resonant frequencies). In practice, however, measurement errors could lead to a
biased trained classifier, potentially resulting in the wrong quality
classifications of the turbine blade. In this study, we investigate the
classification problem of turbine blades under measurement uncertainty. For
this, the concept of Interval Mahalanobis Space is employed, leading to the
Integrated Interval Mahalanobis Classification system (IIMCS) classifier which
has high robustness against measurement uncertainty. The IIMCS employs Binary
Particle Swarm Optimization (BPSO) to filter out those resonant frequencies
which contrib-ute most to the information in the system. A Monte Carlo
Simulation scheme is employed to analyze the sensitivity of the resonant
frequencies to the measurement uncertainty. This yields an indicator of
reliability, indicating the confidence level of the final classification
results. The developed IIMCS methodology is applied to an experimental case
study of equiaxed nickel alloy first-stage turbine blades, showing good and
robust classification performance.
