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Structural health monitoring of composites by combining machine learning and synthetic evaluation methods with vibro-acoustic modulations
Citation Link: https://doi.org/10.15480/882.14123
Publikationstyp
Doctoral Thesis
Date Issued
2025
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2024-12-18
Institute
TORE-DOI
Citation
Technische Universität Hamburg (2025)
The vibro-acoustic modulation (VAM) method is known for its high sensitivity to detect even small damages and is applied non-destructively. VAM uses a high-frequency ultrasonic carrier wave, that is modulated in the specimen by a high-amplitude pump wave of significantly lower frequency. The method was introduced in the 1990s, and the literature today includes over 200 journal publications in which VAM is applied to many laboratory-based applications. However, no industrial application has yet been proclaimed. Since transitioning from the laboratory to the industry is one of the biggest hurdles, this thesis aims to address and overcome the limitations to enable the next step toward industrial applications.
The requirements on the method depend on whether it will be used in the context of non-destructive testing (NDT) or the structural health monitoring (SHM). VAM, as traditionally described in the literature, has no defined baseline of the measurement. Hence, measurements of different specimens can not be reliably compared. In SHM applications, the first measurement is usually assumed to be pristine and is utilised as this baseline. Despite current investigations into estimating a baseline, it remains a major challenge (due to complex signal modulation and dependencies on the material, preexisting damage, \textit{etc.}). To overcome this issue---specifically for NDT applications---this thesis proposes a data-driven approach that is validated with two applications.
First, the adhesive bonding of fibre composite structures is investigated. Single-lap shear specimens with so-called weak bonds and kissing bonds were prepared by inserting a non-stick film, or by contaminating the bond line with a release agent. It is shown that a data-driven evaluation can accurately differentiate between undamaged and damaged specimens in specific frequency ranges, even though the differences based on the traditional evaluation methods are minimal. The trained neural networks are evaluated to recursively generate information on the significance of the input values, leading to a deeper understanding of the VAM method and its mechanisms. The applicability of this data-driven evaluation is confirmed by testing welded steel specimens, where half of them contained crystallisation defects that resulted from false welding parameters.
When VAM is applied as an SHM method, it is advantageous to leverage the ambient vibrations as the modulating pump wave. Ideally, SHM systems are built with low-power and energy-harvesting devices to reduce installation and maintenance costs. The challenge results from variations of the ambient vibration due to changes of environmental influences. Thus, the traditional VAM measurements would neither be consistent nor comparable between the measurements over time.
This challenge is overcome by the proposed synthetic computation of the VAM signal. This synthetic computation minimises the dependency on the ambient vibration so that the VAM measurement can be performed on complex components without interference from environmental changes. The presented approach significantly reduces the requirements on the sensor nodes in terms of sampling rate, measured data points, data size, and energy required to drive the high-frequency emitter. Thus, the usage of self-sustaining energy-harvesting sensor nodes comes into reach. Furthermore, the synthetic method can be implemented into most existing SHM systems that contain acoustic emissions or guided wave measurements with minimal adaptations. Finally, the viability of the synthetic VAM method is demonstrated on larger and more complex structures.
The requirements on the method depend on whether it will be used in the context of non-destructive testing (NDT) or the structural health monitoring (SHM). VAM, as traditionally described in the literature, has no defined baseline of the measurement. Hence, measurements of different specimens can not be reliably compared. In SHM applications, the first measurement is usually assumed to be pristine and is utilised as this baseline. Despite current investigations into estimating a baseline, it remains a major challenge (due to complex signal modulation and dependencies on the material, preexisting damage, \textit{etc.}). To overcome this issue---specifically for NDT applications---this thesis proposes a data-driven approach that is validated with two applications.
First, the adhesive bonding of fibre composite structures is investigated. Single-lap shear specimens with so-called weak bonds and kissing bonds were prepared by inserting a non-stick film, or by contaminating the bond line with a release agent. It is shown that a data-driven evaluation can accurately differentiate between undamaged and damaged specimens in specific frequency ranges, even though the differences based on the traditional evaluation methods are minimal. The trained neural networks are evaluated to recursively generate information on the significance of the input values, leading to a deeper understanding of the VAM method and its mechanisms. The applicability of this data-driven evaluation is confirmed by testing welded steel specimens, where half of them contained crystallisation defects that resulted from false welding parameters.
When VAM is applied as an SHM method, it is advantageous to leverage the ambient vibrations as the modulating pump wave. Ideally, SHM systems are built with low-power and energy-harvesting devices to reduce installation and maintenance costs. The challenge results from variations of the ambient vibration due to changes of environmental influences. Thus, the traditional VAM measurements would neither be consistent nor comparable between the measurements over time.
This challenge is overcome by the proposed synthetic computation of the VAM signal. This synthetic computation minimises the dependency on the ambient vibration so that the VAM measurement can be performed on complex components without interference from environmental changes. The presented approach significantly reduces the requirements on the sensor nodes in terms of sampling rate, measured data points, data size, and energy required to drive the high-frequency emitter. Thus, the usage of self-sustaining energy-harvesting sensor nodes comes into reach. Furthermore, the synthetic method can be implemented into most existing SHM systems that contain acoustic emissions or guided wave measurements with minimal adaptations. Finally, the viability of the synthetic VAM method is demonstrated on larger and more complex structures.
Subjects
SHM
NDT
Vibro-acoustic modulation
Structural health monitoring
VAM
DDC Class
681.2: Testing, Measuring, Sensing Instruments
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