Intelligent Health Indicators Based on Semi-supervised Learning Utilizing Acoustic Emission Data

Morteza Moradi*, Agnes Broer, Juan Chiachío, Rinze Benedictus, Dimitrios Zarouchas

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Health indicators are indices that act as intermediary links between raw SHM data and prognostic models. An efficient HI should satisfy prognostic requirements such as monotonicity, trendability, and prognosability in such a way that it can be effectively used as an input in a prognostic model for remaining useful life estimation. However, discovering or designing a suitable HI for composite structures is a challenging task due to the inherent complexity of the evolution of damage events in such materials. Previous research has shown that data-driven models are efficient for accomplishing this goal. Large labeled datasets, however, are normally required, and the SHM data can only be labeled, respecting prognostic requirements, after a series of nominally identical structures are tested to failure. In this paper, a semi-supervised learning approach based on implicitly imposing prognostic criteria is adopted to design a novel HI suitable. To this end, single-stiffener composite panels were subjected to compression-compression fatigue loading and monitored using acoustic emission (AE). The AE data after signal processing and feature extraction were fused using a multi-layer LSTM neural network with criteria-based hypothetical targets to generate an intelligent HI. The results confirm the performance of the proposed scenario according to the prognostic criteria.

Original languageEnglish
Title of host publicationEuropean Workshop on Structural Health Monitoring, EWSHM 2022, Volume 3
EditorsPiervincenzo Rizzo, Alberto Milazzo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages419-428
Number of pages10
ISBN (Print)9783031073212
DOIs
Publication statusPublished - 2023
Event10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy
Duration: 4 Jul 20227 Jul 2022

Publication series

NameLecture Notes in Civil Engineering
Volume270 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference10th European Workshop on Structural Health Monitoring, EWSHM 2022
Country/TerritoryItaly
CityPalermo
Period4/07/227/07/22

Keywords

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

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