Influence of Environmental Conditions and Damage on Closely Spaced Modes

Jonscher, Clemens; Hofmeister, Benedikt; Grießmann, Tanja; Rolfes, Raimund

doi:10.1007/978-3-031-07322-9_91

Cited by 5 publications

(20 citation statements)

References 11 publications

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“…In this article, the eigenfrequencies and the mode shapes of structure are considered as features, because they are sensitive to the damage states described in Table 1. The Bayesian operational modal analysis (BAYOMA) and mode tracking methods described in Au et al (2013) and Jonscher et al (2023) are used to extract the natural frequencies from the acceleration signals. For a detailed description of the dynamics of the structure, the reader is referred to Wernitz et al (2022), where 15 modes (first 5 bending modes in x -direction, first 5 bending modes in the y -directions, and first 5 torsional modes) were identified from the current sensor setup.…”

Section: The Experimental Dataset From the Lumo Structurementioning

confidence: 99%

“…The dynamic behavior of the LUMO structure, given its dimensions and physical composition, is predisposed to producing closely spaced modes, that is, some of the natural frequencies occur very close to one another. In such cases, smallest system changes are particularly noticeable through a change in the alignment of the bending mode shape in the mode subspace (Jonscher et al, 2023). In addition, the alignment is also subject to the greatest uncertainty.…”

Section: The Experimental Dataset From the Lumo Structurementioning

confidence: 99%

“…Consequently, the mode tracking algorithm used to obtain the natural frequencies can, at times, misclassify the data and introduce large data scatter. This effect is especially prominent during damage (Jonscher et al, 2023; Wickramarachchi et al, 2023). As a result, a number of bending modes that are affected by these misclassifications are excluded from analysis in order to avoid progression of these errors further down the line.…”

Section: The Experimental Dataset From the Lumo Structurementioning

confidence: 99%

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Damage localisation using disparate damage states via domain adaptation

Wickramarachchi,

Gardner,

Poole

et al. 2024

DCE

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A significant challenge of structural health monitoring (SHM) is the lack of labeled data collected from damage states. Consequently, the collected data can be incomplete, making it difficult to undertake machine learning tasks, to detect or predict the full range of damage states a structure may experience. Transfer learning is a helpful solution, where data from (source) structures containing damage labels can be used to transfer knowledge to (target) structures, for which damage labels do not exist. Machine learning models are then developed that generalize to the target structure. In practical applications, it is unlikely that the source and the target structures contain the same damage states or experience the same environmental and operational conditions, which can significantly impact the collected data. This is the first study to explore the possibility of transfer learning for damage localisation in SHM when the damage states and the environmental variations in the source and target datasets are disparate. Specifically, using several domain adaptation methods, this article localizes severe damage states at a target structure, using labeled information from minor damage states at a source structure. By minimizing the distance between the marginal and conditional distributions between the source and the target structures, this article successfully localizes damage states of disparate severities, under varying environmental and operational conditions. The effect of partial and universal domain adaptation—where the number of damage states in the source and target datasets differ—is also explored in order to mimic realistic industrial applications of these methods.

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Section: The Experimental Dataset From the Lumo Structurementioning

confidence: 99%

Section: The Experimental Dataset From the Lumo Structurementioning

confidence: 99%

Section: The Experimental Dataset From the Lumo Structurementioning

confidence: 99%

See 1 more Smart Citation

Damage localisation using disparate damage states via domain adaptation

Wickramarachchi,

Gardner,

Poole

et al. 2024

DCE

Self Cite

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“…However, one of the main challenges with monitoring tower structures is still the identifcation of closely spaced modes, especially in terms of mode shapes. When applying the widely used SSI-COV to tower structures, the problem with closely spaced modes is that the identifed complex mode shapes may have mean phases separated by spatial directions [21][22][23], making it difcult to compare the mode shapes. By contrast, BAY-OMA can be used to identify real mode shapes and will, therefore, also be used in this study.…”

Section: Introductionmentioning

confidence: 99%

“…Te second part of the uncertainty of the mode shape is the alignment of the mode in the MSS. Tis fnding aligns with [21], which examines the mode shapes of a prestressed segmented concrete tower under laboratory conditions using BAYOMA and SSI-COV. It was found that the uncertainty of the alignment identifcation increases signifcantly with the increase in closeness of the frequencies.…”

Section: Introductionmentioning

confidence: 99%

Identification Uncertainties of Bending Modes of an Onshore Wind Turbine for Vibration‐Based Monitoring

Jonscher,

Möller,

Liesecke

et al. 2024

Structural Control and Health Monitoring

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This study considers the identification uncertainties of closely spaced bending modes of an operating onshore concrete-steel hybrid wind turbine tower. The knowledge gained contributes to making mode shapes applicable to wind turbine tower monitoring rather than just mode tracking. One reason is that closely spaced modes make it difficult to determine reliable mode shapes for them. For example, the well-known covariance-driven stochastic subspace identification (SSI-COV) yields complex mode shapes with multiple mean phases in the complex plane, which does not allow error-free transformation to the real space. In contrast, the Bayesian Operational Modal Analysis (BAYOMA) allows the determination of real mode shapes. The application of BAYOMA presents a further challenge when quantifying the associated uncertainties, as the typical assumption of a linear, time-invariant system is violated. Therefore, validity is not self-evident and a comprehensive investigation and comparison of results is required. It has already been shown in a previous study that the significant part of the uncertainty in the mode shapes corresponds to their orientation in the mode subspace (MSS). Despite all the challenges mentioned, there is still a great need to develop reliable monitoring parameters (MPs) for Structural Health Monitoring (SHM). This study contributes to this by analysing metrics for comparing mode shapes. In addition to the well-known Modal Assurance Criteria (MAC), the Second-Order MAC (S2MAC) is also used to eliminate the alignment uncertainty by comparing the mode shape with a MSS. In addition, the mode shape identification uncertainties of BAYOMA are also considered. Including uncertainties is also essential for the typically used natural frequencies and damping ratios, which can be more appropriately used if the identification uncertainty is known.

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