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Experimental validation of the proposed technique for condition monitoring of structure using limited noisy modal data

Experimental validation of the proposed technique for condition monitoring of structure using... Condition monitoring (CM) of structures is important from safety consideration. Damage detection techniques, using inverse dynamic approaches, are important tools to improve the mathematical models for monitoring the condition of structure. Uncertainties in the measured data might lead to unreliable identification of damage in structural system. Experimental validation is crucial for establishing its practical applicability. The measurement of dynamic responses at all degrees of freedom (DOFs) of a structure is also not feasible in practice. In addition the effect of these uncertainties and constraint of limited measurement are required to be studied based on experimental validation. This paper aims to discuss these issues.Design/methodology/approachProposed numerical model based on measured natural frequencies and mode shapes is found suitable for CM of framed structures in the framework of finite element model with limited dynamic responses. The structural properties, namely, axial rigidity and bending rigidity are identified at the element level in the updated models of the system. Damage at the element level is identified by comparing the identified structural parameters of the updated model of the system with those of the undamaged state. Proposed numerical model is suitable for practical problem, as it is able to identify the structural parameters with limited modal data of first few modes, measured at selected DOFs.FindingsThe model is able to identify the structural damage with greater accuracy from the noisy dynamic responses even if the extent of damage is small. Experimental studies, on simple cantilever beams, establish the potential of the proposed methods for its practical implementation.Research limitations/implicationsThe greater random noise will lead to unreliable identification of structural parameters as observed. Thus, filtering of noise technique may be required to be adopted prior to consideration of the measured data in the proposed identification approach.Practical implicationsRequirement of higher modal data seems to be difficult in case of real life practical problem. Thus, simulation technique like condensation or SEREP technique may be adopted.Social implicationsStructural health monitoring of infrastructural system is significantly important. CM of those structures from global response with limited measured data seems to be an effective tool to ensure safety and durability of structures.Originality/valueThe modal testing and subsequent extraction of modal data have been carried out at the authors’ laboratory. The numerical code based on inverse dynamic approach has been developed independently with original contribution. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Structural Integrity Emerald Publishing

Experimental validation of the proposed technique for condition monitoring of structure using limited noisy modal data

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References (17)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1757-9864
DOI
10.1108/ijsi-02-2019-0013
Publisher site
See Article on Publisher Site

Abstract

Condition monitoring (CM) of structures is important from safety consideration. Damage detection techniques, using inverse dynamic approaches, are important tools to improve the mathematical models for monitoring the condition of structure. Uncertainties in the measured data might lead to unreliable identification of damage in structural system. Experimental validation is crucial for establishing its practical applicability. The measurement of dynamic responses at all degrees of freedom (DOFs) of a structure is also not feasible in practice. In addition the effect of these uncertainties and constraint of limited measurement are required to be studied based on experimental validation. This paper aims to discuss these issues.Design/methodology/approachProposed numerical model based on measured natural frequencies and mode shapes is found suitable for CM of framed structures in the framework of finite element model with limited dynamic responses. The structural properties, namely, axial rigidity and bending rigidity are identified at the element level in the updated models of the system. Damage at the element level is identified by comparing the identified structural parameters of the updated model of the system with those of the undamaged state. Proposed numerical model is suitable for practical problem, as it is able to identify the structural parameters with limited modal data of first few modes, measured at selected DOFs.FindingsThe model is able to identify the structural damage with greater accuracy from the noisy dynamic responses even if the extent of damage is small. Experimental studies, on simple cantilever beams, establish the potential of the proposed methods for its practical implementation.Research limitations/implicationsThe greater random noise will lead to unreliable identification of structural parameters as observed. Thus, filtering of noise technique may be required to be adopted prior to consideration of the measured data in the proposed identification approach.Practical implicationsRequirement of higher modal data seems to be difficult in case of real life practical problem. Thus, simulation technique like condensation or SEREP technique may be adopted.Social implicationsStructural health monitoring of infrastructural system is significantly important. CM of those structures from global response with limited measured data seems to be an effective tool to ensure safety and durability of structures.Originality/valueThe modal testing and subsequent extraction of modal data have been carried out at the authors’ laboratory. The numerical code based on inverse dynamic approach has been developed independently with original contribution.

Journal

International Journal of Structural IntegrityEmerald Publishing

Published: Jan 22, 2020

Keywords: Noise; Damage; Condition monitoring; Inverse dynamic approach; Modal data

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