A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Contributions to Acoustic Emission-based Structural Health Monitoring and Process Monitoring
Accurate in-situ measurements and suitable signal processing are important for reliable monitoring and control of modern industrial equipment. However, measuring relevant variables may be difficult due to physical and technological limitations. Acoustic Emission (AE), which refers to passively recorded mechanical stress waves using highly sensitive equipment, is frequently suggested to obtain in-situ measurements. For instance, AE can be detected due to the release of elastic energy in a material under load due to damage. Sources of AE are manifold and different source mechanisms can be distinguished using suitable signal processing. Furthermore, advantages such as high sensitivity to incipient damages, detectability of damages in a distance from the source, and characterization and localization of source mechanisms, are inherent to the measurement principle. Hence, AE can be identified as promising in-situ measurement technology. The two main research areas (i) Structural Health Monitoring (SHM) and (ii) process monitoring and control are considered in this thesis. From a methodological point of view, both fields are related in that similar methods are used for analysis and interpretation of AE. Several research questions, which address key topics in AE-based monitoring, are established. In particular, suitable sensor mounting is crucial for successful detection of AE. However, some mounting techniques, which were successfully used in the laboratory, may be difficult to realize in practice. Therefore, the question raises if there are alternative mounting strategies, which are suitable to simplify the application of AE. Regarding the use of machine learning for analysis of AE it remains as an open question, how the reliability of an individual classification result depends on environmental and operational factors. Furthermore, characterizing the effect of variable operational and environmental conditions on AE signatures is a difficult topic. Regarding the use of AE for process monitoring and control, it can be ...
Contributions to Acoustic Emission-based Structural Health Monitoring and Process Monitoring
Accurate in-situ measurements and suitable signal processing are important for reliable monitoring and control of modern industrial equipment. However, measuring relevant variables may be difficult due to physical and technological limitations. Acoustic Emission (AE), which refers to passively recorded mechanical stress waves using highly sensitive equipment, is frequently suggested to obtain in-situ measurements. For instance, AE can be detected due to the release of elastic energy in a material under load due to damage. Sources of AE are manifold and different source mechanisms can be distinguished using suitable signal processing. Furthermore, advantages such as high sensitivity to incipient damages, detectability of damages in a distance from the source, and characterization and localization of source mechanisms, are inherent to the measurement principle. Hence, AE can be identified as promising in-situ measurement technology. The two main research areas (i) Structural Health Monitoring (SHM) and (ii) process monitoring and control are considered in this thesis. From a methodological point of view, both fields are related in that similar methods are used for analysis and interpretation of AE. Several research questions, which address key topics in AE-based monitoring, are established. In particular, suitable sensor mounting is crucial for successful detection of AE. However, some mounting techniques, which were successfully used in the laboratory, may be difficult to realize in practice. Therefore, the question raises if there are alternative mounting strategies, which are suitable to simplify the application of AE. Regarding the use of machine learning for analysis of AE it remains as an open question, how the reliability of an individual classification result depends on environmental and operational factors. Furthermore, characterizing the effect of variable operational and environmental conditions on AE signatures is a difficult topic. Regarding the use of AE for process monitoring and control, it can be ...
Contributions to Acoustic Emission-based Structural Health Monitoring and Process Monitoring
Wirtz, Sebastian Felix (author) / Söffker, Dirk
2020-07-10
Theses
Electronic Resource
English
Contributions to Acoustic Emission-based Structural Health Monitoring and Process Monitoring
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