Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data
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Elsevier
Materia
Prognostic and health management Structural health monitoring Intelligent health indicator Semi-supervised deep neural network Composite structures
Date
2022-11-01Referencia bibliográfica
Morteza Moradi... [et al.]. Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data, Engineering Applications of Artificial Intelligence, Volume 117, Part A, 2023, 105502, ISSN 0952-1976, [https://doi.org/10.1016/j.engappai.2022.105502]
Sponsorship
European Commission 859957 769288Abstract
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.