Deep Convolutional Neural Network‐Based Structural Damage Localization and Quantification Using Transmissibility Data
Open accessAbstract
Damage diagnosis has become a valuable tool for asset management, enhanced by advances in sensor technologies that allows for system monitoring and providing massive amount of data for use in health state diagnosis. However, when dealing with massive data, manual feature extraction is not always a suitable approach as it is labor intensive requiring the intervention of domain experts with knowledge about the relevant variables that govern the system and their impact on its degradation process. To address these challenges, convolutional neural networks (CNNs) have been recently proposed to automatically extract features that best represent a system’s degradation behavior and are a promising and powerful technique for supervised learning with recent studies having shown their advantages for feature identification, extraction, and damage quantification in machine health assessment. Here, we propose a novel deep CNN‐based approach for structural damage location and quantification, which operates on images generated from the structure’s transmissibility functions to exploit the CNNs’ image processing capabilities and to automatically extract and select relevant features to the structure’s degradation process. These feature maps are fed into a multilayer perceptron to achieve damage localization and quantification. The approach is validated and exemplified by means of two case studies involving a mass‐spring system and a structural beam where training data are generated from finite element models that have been calibrated on experimental data. For each case study, the models are also validated using experimental data, where results indicate that the proposed approach delivers satisfactory performance and thus being an appropriate tool for damage diagnosis.
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Transmissibility functions have a strong relationship with system damages and have been proposed as damage sensitive features.
“TFs have been shown to have a strong relationship with system damages and, as such, they have been proposed as damage sensitive features in several studies in the literature.”
Transmissibility functions depend only on the location of the excitation load, not on its magnitude.
“The main advantage of TFs is that they only depend on the location of the excitation load, but not on its magnitude.”
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Structural Damage Identification from Acceleration Wavelet Data Using Convolutional Neural Networks
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Frameworks for interpretability of deep learning-based prognostics and health management
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A Quantitative Diagnosis Method for Aero-Engine Blade Cracks Based on 3D-BTC
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- Authors
- Cofre-Martel, S., Kobrich, P., Lopez Droguett, E., & Meruane, V.
- Affiliations
- University of Maryland, College Park (US)
- University of Chile (CL)
- Venue
- Shock and Vibration
- Publisher
- Wiley
- Published
- 2019-01
- Volume
- 2019
- Issue
- 1
- ISSN
- 1070-9622 (ISSN-L)
- 1875-9203
- OpenAlex
- W2971966787
- Cited by
- 65
- References
- 47
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 9 · 2023: 13 · 2022: 14 · 2021: 12 · 2020: 6 · 2019: 3
- FWCI
- 6.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Shock and Vibration (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fondo Nacional de Desarrollo Científico y Tecnológico (1160494, 1170535)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- structural damage localization, damage quantification, transmissibility function, convolutional neural networks, automatic feature extraction, damage diagnosis, multilayer perceptron, mass-spring system, structural beams, finite element model updating, structural health monitoring
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