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Structural damage identification using single-point vibration data processing

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Pubblicato in:PLoS One vol. 20, no. 9 (Sep 2025), p. e0330909
Autore principale: Huan-Yi Chu
Altri autori: Meng-Hsuan Tien
Pubblicazione:
Public Library of Science
Soggetti:
Data processing
Deep learning
Dynamic response
Vibration monitoring
Identification methods
Vibration measurement
Identification
Artificial neural networks
Nonlinear analysis
Neural networks
Safety engineering
Damage detection
Cracks
Feasibility studies
Structural damage
Fourier transforms
Vibration
Structural health monitoring
Machine learning
Parameter identification
Data augmentation
Artificial intelligence
Spectrograms
Sensors
Vibration analysis
Algorithms
Dynamical systems
Nonlinear dynamics
Environmental
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Abstract:Structural health monitoring and damage identification are essential for ensuring the safety and performance of engineering systems. Cracks introduce nonlinear dynamic behavior due to intermittent contact from the opening and closing of crack surfaces, which limits the effectiveness of conventional linear identification methods. Moreover, many existing approaches rely on multiple distributed sensors, which may be impractical in real-world applications. To address these limitations, this study investigates the feasibility of identifying both crack depth and location using single-point vibration measurements. A recently developed nonlinear analysis framework is employed to simulate the dynamic response of a cracked beam, and spectrograms of the tip response under various crack conditions are generated using the short-time Fourier transform. These spectrograms are then used to train a convolutional neural network for damage identification. Numerical results demonstrate that the proposed method achieves high coefficients of determination () between the true and identified values for both crack depth and location, provided the training data sufficiently cover damage conditions within the defined parameter ranges. Furthermore, data augmentation is shown to enhance identification accuracy, underscoring the method’s potential for implementation with limited vibration measurements.
ISSN:1932-6203
DOI:10.1371/journal.pone.0330909
Fonte:Health & Medical Collection