TY - JOUR
T1 - A novel intelligent health indicator using acoustic waves
T2 - CEEMDAN-driven semi-supervised ensemble deep learning
AU - Moradi, Morteza
AU - Galanopoulos, Georgios
AU - Kuiters, Thyme
AU - Zarouchas, Dimitrios
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Acoustic emission
KW - Ensemble learning
KW - Intelligent health indicator
KW - Prognostics and health management
KW - Semi-supervised deep neural network
UR - http://www.scopus.com/inward/record.url?scp=85210753871&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2024.112156
DO - 10.1016/j.ymssp.2024.112156
M3 - Article
AN - SCOPUS:85210753871
SN - 0888-3270
VL - 224
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
M1 - 112156
ER -