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

Liangliang Cheng*, V. Yaghoubi, W. Van Paepegem, M. Kersemans

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


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.

Original languageEnglish
Pages (from-to)166-179
Number of pages14
JournalStructural Health Monitoring
Issue number1
Publication statusPublished - 2022
Externally publishedYes


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

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