4.7 Article

Integrated interval Mahalanobis classification system for the quality classification of turbine blades based on vibrational data incorporating measurement uncertainty

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Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/14759217221076366

Keywords

integrated Mahalanobis classification system; interval Mahalanobis distance; feature selection; classification; binary particle swarm optimization; non-destructive testing; uncertainty propagation; Monte Carlo method

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This paper proposes a novel Integrated Interval Mahalanobis Classification System (IIMCS) to accurately classify turbine blades based on vibrational response data with measurement uncertainty.
Measurements are not exactly accurate, and measurement errors could lead to a biased trained classifier, and finally to a wrong classification of the parts. This paper extends the recently proposed (Integrated) Mahalanobis Classification System with the concept of Interval Mahalanobis distance (IMD) in order to account for measurement uncertainty. This novel Integrated Interval Mahalanobis Classification System (IIMCS) is applied to an experimental case study of complex shaped metallic turbine blades with various damage types. The turbine blades have been vibrationally tested in a wide frequency range. The IIMCS selects a subset of optimal features that contribute the most to the system under the framework of Binary Particle Swarm Optimization, and determines the optimal decision threshold based on Particle Swarm Optimizer. A Monte Carlo method (MCM) is implemented to account for measurement uncertainty, and as such yields an indicator of reliability, implying the confidence level of the classification results. The obtained results illustrate a high performance of the IIMCS for classifying turbine blades based on vibrational response data with measurement uncertainty.

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