Data-Driven Maintenance Decision Analytics Using Multimodal Structural Health Indicators for Composite Asset Fleets

Authors

  • Yisong Chen 1 https://orcid.org/0009-0009-4978-7565
    • Conceptualization
    • Formal Analysis
    • Investigation
    • Methodology
    • Resources
    • Writing – Original Draft Preparation
    • Writing – Review & Editing
    • Data Curation
    • Project Administration
    • Supervision
    • Validation
    • Visualization
  • Chuanhao Nie 1,* https://orcid.org/0009-0006-4208-6414
    • Conceptualization
    • Data Curation
    • Methodology
    • Project Administration
    • Writing – Original Draft Preparation
    • Writing – Review & Editing
  • Yexin Tian 1 https://orcid.org/0009-0000-7873-6681
    • Data Curation
    • Investigation
    • Methodology
    • Software
    • Validation
    • Visualization
    • Writing – Review & Editing
1 College of Computing, Georgia Institute of Technology, Atlanta, GA, USA 30332
Corresponding author: cnie30@gatech.edu

DOI:

https://doi.org/10.63646/EWUD8253

Keywords:

Composite structures; Data-driven analytics; Structural health monitoring; Multimodal sensor fusion; Prognostics and health management; Maintenance decision analytics; Health indicators; Remaining useful life

Abstract

The safety, airworthiness, and lifecycle cost efficiency of composite asset fleets depend critically on the ability to derive reliable, interpretable health indicators (HIs) that integrate heterogeneous sensing modalities. Traditional single-modal approaches based on acoustic emission (AE) or guided-wave (GW) interrogation alone suffer from limited representativeness, poor generalization across fleet units, and insufficient monotonicity under stochastic damage accumulation. This study presents a comprehensive data-driven maintenance decision analytics framework that constructs multimodal structural health indicators (SHIs) by fusing passive acoustic emission signals and active guided-wave damage indices. The framework encompasses four integrated layers: multimodal data acquisition and synchronization, modality-specific feature engineering, semi-supervised inter-modality fusion, and fleet-level maintenance decision analytics. Prognostic quality criteria—including monotonicity (Mo), prognosability (Pr), and trendability (Tr)—are embedded directly into the learning objectives to yield health trajectories suitable for remaining useful life (RUL) estimation and maintenance scheduling. Experimental results on a composite panel fatigue dataset demonstrate that the proposed multimodal SHI achieves composite prognostic scores exceeding 0.88, substantially outperforming single-modal baselines. Fleet-level decision analytics translates SHI outputs into condition-based maintenance triggers, demonstrating a 23.4% reduction in unplanned downtime relative to fixed-interval scheduling. The findings underscore the practical value of multimodal fusion for industrial prognostics and health management (PHM) of composite structures.

How to Cite

Chen, Y., Nie, C., & Tian, Y. (2026). Data-Driven Maintenance Decision Analytics Using Multimodal Structural Health Indicators for Composite Asset Fleets. Journal of Business and Data Analytics, 4(9), 24-39. https://doi.org/10.63646/EWUD8253

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