A novel intelligent health indicator using acoustic waves: CEEMDAN-driven semi-supervised ensemble deep learning

Morteza Moradi*, Georgios Galanopoulos, Thyme Kuiters, Dimitrios Zarouchas

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Designing health indicators (HIs) for aerospace composite structures that demonstrate their health comprehensively, including all types of damage that can be adaptively updated, is challenging, especially under complex conditions like impact and compression-fatigue loadings. This paper introduces a new AI-based approach to designing reliable HIs (fulfilling requirements—monotonicity, prognosability, and trendability—referred to as ’Fitness’) for single-stiffener composite panels under fatigue loading using acoustic emission sensors. It incorporates complete ensemble empirical mode decomposition with adaptive noise for feature extraction, semi-supervised base deep learner models made of long short-term memory layers for information fusion, and a semi-supervised paradigm to simulate labels inspired by the physics of progressive damage. In this way, nondifferentiable prognostic criteria are implicitly implemented into the learning process. Ensemble learning, especially using a semi-supervised network built with bidirectional long short-term memory, improves HI quality while reducing deep learning randomness. The Fitness function equation has been modified to provide a more trustworthy foundation for comparison and enhance the practical reliability of the standard in prognostics and health management. Ablation experiments are conducted, including variations in dataset division and leave-one-out cross-validation, confirming the generalizability of the approach.

Original languageEnglish
Article number112156
JournalMechanical Systems and Signal Processing
Volume224
DOIs
Publication statusPublished - 2025

Keywords

  • Acoustic emission
  • Ensemble learning
  • Intelligent health indicator
  • Prognostics and health management
  • Semi-supervised deep neural network

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