Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data

Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data

Moradi, M. (TU Delft Structural Integrity & Composites)
Broer, Agnes A.R. (TU Delft Structural Integrity & Composites)
Chiachío, Juan (University of Granada)
Benedictus, R. (TU Delft Structural Integrity & Composites)
Loutas, Theodoros H. (University of Patras)
Zarouchas, D. (TU Delft Structural Integrity & Composites)

2023

A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic criteria, i.e., monotonicity, trendability, and prognosability, that are commonly utilized to measure the HI's quality. However, constructing such a HI is challenging, particularly for composite structures due to their vulnerability to complex damage scenarios. Data-driven models and deep learning are powerful mathematical tools that can be employed to achieve this purpose. Yet the availability of a large dataset with labels plays a crucial role in these fields, and the data collected by SHM methods can only be labeled after the structure fails. In this respect, semi-supervised learning can incorporate unlabeled data monitored from structures that have not yet failed. In the present work, a semi-supervised deep neural network is proposed to construct HI by SHM data fusion. For the first time, the prognostic criteria are used as targets of the network rather than employing them only as a measurement tool of HI's quality. In this regard, the acoustic emission method was used to monitor composite panels during fatigue loading, and extracted features were used to construct an intelligent HI. Finally, the proposed roadmap is evaluated by the holdout method, which shows a 77.3% improvement in the HI's quality, and the leave-one-out cross-validation method, which indicates the generalized model has at least an 81.77% score on the prognostic criteria. This study demonstrates that even when the true HI labels are unknown but the qualified HI pattern (according to the prognostic criteria) can be recognized, a model can still be built that provides HIs aligning with the desired degradation behavior.

Composite structures
Intelligent health indicator
Prognostic and health management
Semi-supervised deep neural network
Structural health monitoring

http://resolver.tudelft.nl/uuid:ac148328-5b6f-495c-a862-cc16bd0610ce

https://doi.org/10.1016/j.engappai.2022.105502

0952-1976

Engineering Applications of Artificial Intelligence, 117

Institutional Repository

journal article

© 2023 M. Moradi, Agnes A.R. Broer, Juan Chiachío, R. Benedictus, Theodoros H. Loutas, D. Zarouchas