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Research Papers

Output-Only Damage Identification Using Enhanced Structural Characteristic Deflection Shapes and Adaptive Gapped Smoothing Method

[+] Author and Article Information
Shancheng Cao

Centre for Engineering Dynamics,
School of Engineering,
The University of Liverpool,
Liverpool L69 3GH, UK
e-mail: caoshch@liv.ac.uk

Huajiang Ouyang

Centre for Engineering Dynamics,
School of Engineering,
The University of Liverpool,
Liverpool L69 3GH, UK
e-mail: h.ouyang@liverpool.ac.uk

1Corresponding author.

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received September 14, 2016; final manuscript received June 23, 2017; published online September 1, 2017. Assoc. Editor: Stefano Lenci.

J. Vib. Acoust 140(1), 011005 (Sep 01, 2017) (11 pages) Paper No: VIB-16-1458; doi: 10.1115/1.4037469 History: Received September 14, 2016; Revised June 23, 2017

Structural characteristic deflection shapes (CDSs) such as mode shapes which contain spatial knowledge of structures are highly sensitive for damage detection and localization. Nevertheless, CDSs are vulnerable to measurement noise, which degrades the accuracy of damage identification. In order to enhance CDS-based damage identification, contributions are made in three aspects. First, a robust CDS estimation approach is proposed based on common principal component analysis, which estimates the CDSs as the common diagonalizer of a set of covariance matrices by joint approximation diagonalization (JAD). Second, an adaptive gapped smoothing method (GSM) is proposed and validated to be more accurate than the traditional GSM. Third, a new damage identification index capable of localizing damage and indicating relative damage severity is defined without requiring information of healthy structures. Finally, numerical and experimental examples of beams and a frame with cracks are studied to demonstrate the advantages of the proposed damage identification method in terms of noise robustness and accuracy.

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References

Figures

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Fig. 1

Singular value plot of numerical example 1 in Sec. 4

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Fig. 2

Illustration of the adaptive GSM

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Fig. 3

Meshed cantilever beam with two open cracks of 20% depth

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Fig. 4

The relative errors  Eϕ with nlevel=10%: (a) first CDS, (b) second CDS, and (c) third CDS

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Fig. 5

CDSs and their curvatures of a beam with two open cracks of 20% depth

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Fig. 6

DI values of a beam with two open cracks of three depth levels: (a) 2% depth using adaptive GSM, (b) 20% depth using adaptive GSM, (c) 2% depth using GSM, and (d) 20% depth using GSM

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Fig. 7

Meshed cantilever beam with two breathing cracks of 20% depth

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Fig. 8

CDS’s and their curvatures of a beam with two breathing cracks of 20% depth

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Fig. 9

DI values of a beam with two breathing cracks of three depth levels: (a) 2% depth using adaptive GSM, (b) 20% depth using adaptive GSM, (c) 2% depth using GSM, and (d) 20% depth using GSM

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Fig. 10

A two-storey frame structure with two breathing cracks of 10% depth

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Fig. 11

Damage identification results: (a) breathing crack 1 and (b) breathing crack 2

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Fig. 12

An experimental cantilever beam with two cracks

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Fig. 13

Power spectral density: (a) output signal of PSV 500 and (b) practical input force

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Fig. 14

Singular values of Ry¯y¯(0) in descending order

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Fig. 15

CDS’s and their curvatures of a beam with two cracks of 20% depth

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Fig. 16

DI values of a beam with two cracks of 20% depth: (a) adaptive GSM and (b) GSM

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Fig. 17

DI values of a beam with two cracks of 30% depth: (a) adaptive GSM and (b) GSM

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