Identifying Multidimensional Damage in a Hierarchical Dynamical System

Chelidze, David

doi:10.1023/b:nody.0000045546.02766.ad

Cited by 33 publications

(59 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In Chelidze (2004), for developing multidimensional tracking vector, it was proposed to partition phase space into disjoint small hypercuboids (B i , iZ1, ., N d ) and evaluate the expected value of the PSWF in each of these regions, where semicolons indicate column-wise concatenation of each e i . In Chelidze (2004), it was also demonstrated that these feature vectors can be projected onto actual damage states if the total change in the damage state was small.…”

Section: Theorymentioning

confidence: 99%

“…Therefore, it provides needed information for constructing or validating slow-time predictive models, which can directly use available fast-time measurements. In previous works, it was demonstrated that, in a stationary operating environment, this approach can be successful for identifying a fatigue damage (Chelidze & Cusumano 2004Chelidze & Liu 2005) or for multidimensional drift tracking (Chelidze 2004;Chelidze & Liu 2006). In both of these cases, phase space warping (PSW)-based feature vectors were analysed using smooth orthogonal decomposition (SOD) to identify the slow processes.…”

Section: Introductionmentioning

confidence: 99%

“…Previous works (Chelidze 2004;Chelidze & Liu 2005Chelidze & Cusumano 2006) include detailed reviews of the corresponding technologies. Some of the recent developments are in the area of damage diagnosis under time-varying environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, one needs an ability to separate variabilities caused by damage and changes in the environmental and operating conditions. Much of the previous work (Chelidze 2004;Chelidze & Liu 2006) was devoted to addressing the first two requirements by developing PSW-based feature vectors and the SOD-based damage identification. This paper focuses on the final requirement.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reconstructing slow-time dynamics from fast-time measurements

Chelidze

Liu

2007

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

This paper considers a dynamical system subjected to damage evolution in variable operating conditions to illustrate the reconstruction of slow-time (damage) dynamics using fast-time (vibration) measurements. Working in the reconstructed fast-time phase space, phase space warping-based feature vectors are constructed for slow-time damage identification. A subspace of the feature space corresponding to the changes in the operating conditions is identified by applying smooth orthogonal decomposition (SOD) to the initial set of feature vectors. Damage trajectory is then reconstructed by applying SOD to the feature subspace not related to the changes in the operating conditions. The theory is validated experimentally using a vibrating beam, with a variable nonlinear potential field, subjected to fatigue damage. It is shown that the changes in the operating condition (or the potential field) can be successfully separated from the changes caused by damage (or fatigue) accumulation and SOD can identify the slow-time damage trajectory.

show abstract

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconstructing slow-time dynamics from fast-time measurements

Chelidze

Liu

2007

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…SOD can be thought of as an extension of POD [14][15][16][17][18][19][20][21]. In addition to considering spatial (i.e., statistical) characteristics of the data set as in POD, SOD considers temporal (i.e., dynamical) characteristics of the data set as well.…”

Section: Introductionmentioning

confidence: 99%

Robust and Dynamically Consistent Model Order Reduction for Nonlinear Dynamic Systems

Segala

Chelidze

2014

Journal of Dynamic Systems, Measurement, and Control

Self Cite

View full text Add to dashboard Cite

There is a great importance for faithful reduced order models (ROMs) that are valid over a range of system parameters and initial conditions. In this paper, we demonstrate through two nonlinear dynamic models (pinned-pinned beam and thin plate) that are both randomly and periodically forced that smooth orthogonal decomposition (SOD)-based ROMs are valid over a wide operating range of system parameters and initial conditions when compared to proper orthogonal decomposition (POD)-based ROMs. Two new concepts of subspace robustness-the ROM is valid over a range of initial conditions, forcing functions, and system parameters-and dynamical consistency-the ROM embeds the nonlinear manifold-are used to show that SOD, as opposed to POD, can capture the low order dynamics of a particular system even if the system parameters or initial conditions are perturbed from the design case.

show abstract

Practical Method for Tracking Fatigue Damage

Son

2021

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Identifying Multidimensional Damage in a Hierarchical Dynamical System

Cited by 33 publications

References 24 publications

Reconstructing slow-time dynamics from fast-time measurements

Reconstructing slow-time dynamics from fast-time measurements

Robust and Dynamically Consistent Model Order Reduction for Nonlinear Dynamic Systems

Practical Method for Tracking Fatigue Damage

Contact Info

Product

Resources

About