Dual testing field for studies of environmental and operational effects in structural damage localization of mechanical structures

Rohrer, Maximilian

Dual testing field for studies of environmental and operational effects in structural damage localization of mechanical structures

Contributing person datacite.contributor.HostingInstitution	I4S at the Universitiy of Applied Sciences
Contributing person datacite.contributor.ProjectMember	Max Moeller
Contributing person datacite.contributor.RightsHolder	I4S at the Universitiy of Applied Sciences
Contributing person datacite.contributor.Supervisor	Armin Lenzen
Documentation of the data datacite.description.TechnicalInfo	Please see the provided Datasheet.
Additional geographical or spatial references datacite.geolocation	Leipzig
Description of the data datacite.resourceType	The dataset provides vibration data of a dual testing field under ambient excitation. The dual testing field consists of a laboratory setup with constant conditions and a field setup with varying conditions. Both setups are mechanical identical and equipped with 24 piezoelectric acceleration sensors PCB393A03. The dataset contains various mass alteration studies under constant conditions (laboratory setup) and varying conditions (field setup). Environmental data (i.e., air temperature, surface temperature, wind velocity, wind direction, rain events) is supplied for the field setup. The measurements are of 10 min length and downsampled to 1 kHz sample frequency (initial measurement sample frequency 10 kHz).
Type of the data datacite.resourceTypeGeneral	Dataset
Total size of the dataset datacite.size	448705101270
Author dc.contributor.author	Rohrer, Maximilian
Upload date dc.date.accessioned	2024-07-09T06:58:01Z
Publication date dc.date.available	2024-07-09T06:58:01Z
Data of data creation dc.date.created	2023
Publication date dc.date.issued	2024-07-09
Abstract of the dataset dc.description.abstract	This dataset accompanies the research on the impact of Environmental and Operational Conditions (EOC) on vibration-based Structural Health Monitoring (SHM) methods. It includes comprehensive acceleration data collected from a novel experimental testing field consisting of two identical mechanical structures. One structure operates in a controllable laboratory environment, while the other is subjected to real-world EOC in a field setup. The dataset captures mass along with various environmental factors affecting the field setup. This modular measurement system ensures the collection of high-quality data, making this dataset a valuable benchmark for researchers studying the effects of EOC on SHM. The dataset provides a unique opportunity for validating and developing robust SHM techniques that can adapt to varying EOC, fostering advancements in the field.
Public reference to this page dc.identifier.uri	https://opara.zih.tu-dresden.de//handle/123456789/718
Public reference to this page dc.identifier.uri	https://doi.org/10.25532/OPARA-524
Publisher dc.publisher	Technische Universität Dresden
Licence dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	en
URI of the licence text dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/
Specification of the discipline(s) dc.subject.classification	4::41::402::402-02
Title of the dataset dc.title	Dual testing field for studies of environmental and operational effects in structural damage localization of mechanical structures
Research instruments opara.descriptionInstrument	PCB393A03
Project abstract opara.project.description	Part of the DFG (German Research Council) focus program “SPP100+”, subproject: ADMO — Automatic Data-Drive Modeling and H2/H∞-Norm-based Dimension Reduction of Process-Oriented and cooperative systems for SHM-Condition analysis with Methods of System Identification and Machine Learning on Exposed Structures The digital change is causing profound changes in all areas of society. In the fusion of BIM, the optimized planning, execution and management of plants, buildings and infrastructures, with Structural Health Monitoring (SHM) a digital twin functions as a central element of an efficient data organization. The aim of this project is a method that realizes automated data-driven modeling based on the H2/H-infinite norm and methods of system identification coupled with machine learning. This enables a condition monitoring as a digital twin over the service life of the real twin, the building, which is incorporated into an SHM/BIM concept. Based on process-oriented cooperative systems, special physically interpretable indicators are able to automatically display and localize structural changes. The numerical method works with stochastic multi-correlated output-only measurement data, with special consideration and classification of environmental and operational conditions. The automatically generated parameterized stochastic process models of the system and filter theory enables a prediction of future damage states on the examined structure. This gives the public authority a set of tools for predictive planning of maintenance measures on structures with high economic benefits. The research was conducted at the I4S — The Institute for Statics, Structural Dynamics, System Identification, and Simulation at the University of Applied Sciences.
Public project website(s) opara.project.publicReference	https://i4s.htwk-leipzig.de/en/research/current-research-projects/automatic-data-driven-modeling-with-system-identification-methods
Public project website(s) opara.project.publicReference	https://www.spp100plus.de/forschung/cluster-c/teilprojekt-c03/
Public project website(s) opara.project.publicReference	https://i4s.htwk-leipzig.de/en/research/current-research-projects
Project title opara.project.title	ADMO - Automatic Data-Drive Modeling and H2/H∞-Norm-based Dimension Reduction of Process-Oriented and cooperative systems for SHM-Condition analysis with Methods of System Identification and Machine Learning on Exposed Structures

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Description:: Catalog of all laboratory measurements.

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Description:: Laboratory measurements with additional mass of 11.3 kg at Pos 1-2.

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Description:: Laboratory measurements with additional mass of 19.9 kg at Pos 1-2.

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Description:: Laboratory measurements with additional mass of 2.6 kg at Pos 5-6.

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Description:: Laboratory measurements with additional mass of 4.2 kg at Pos 5-6.

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Description:: Laboratory measurements with additional mass of 11.3 kg at Pos 5-6.

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Description:: Laboratory measurements with additional mass of 19.9 kg at Pos 5-6.

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