Dr. Kumar Vikram Singh Profile

Dr. Kumar Vikram Singh

at Miami Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 11 April 2007 Paper

Detection of minuscule changes in structures from spectral data

Kumar Vikram Singh, Gopal Krishna Srinivas, Amit Shukla, Su-Seng Pang

Proceedings Volume 6532, 65321F (2007) https://doi.org/10.1117/12.716049

KEYWORDS: Structural health monitoring, Matrices, Inverse problems, Data modeling, Systems modeling, Mechanical engineering, Modal analysis, Error analysis, Algorithm development, Chemical elements

Read Abstract +

The spectral data i.e. eigenvalues (natural-frequencies) and eigenvectors (mode-shapes), characterizes the dynamics of the system. The dynamic analysis of physical systems leads to certain direct and inverse eigenvalue problems. The direct eigenvalue problem deals in evaluating the spectral behavior of structures for given distributions of physical parameters such as mass, area, stiffness etc. whereas, the estimation of these physical parameters form the spectral data is known as inverse eigenvalue problem. The detection of minuscule (small) changes in the stiffness and mass of the structure, by solving certain inverse eigenvalue problems, is addressed here by considering a grooved axially vibrating rod. In solving direct problems, we have considered two types of eigenvalue problem: (i) traditional algebraic eigenvalue problems and (ii) transcendental eigenvalue problems associated with the continuous system. In conclusion, we have (a) obtained the eigenvalues of damaged rod, (b) analyzed the behavior of the spectral data due to minuscule change in the physical parameters, and (c) determined the different type of spectral data that are required for detecting damage parameters. Several numerical examples are solved here demonstrating the feasibility and accuracy of the identification technique by solving Transcendental Inverse Eigenvalue Problems.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years