time serie STR DID math algorithm

time serie STR DID math algorithm - K. Krishnan Nair Ph.D....

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
K. Krishnan Nair Ph.D. Student John A. Blume Earthquake Engineering Center, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305 e-mail: kknair@stanford.edu Anne S. Kiremidjian 1 Professor Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305 e-mail: ask@stanford.edu Time Series Based Structural Damage Detection Algorithm Using Gaussian Mixtures Modeling In this paper, a time series based detection algorithm is proposed utilizing the Gaussian Mixture Models. The two critical aspects of damage diagnosis that are investigated are detection and extent. The vibration signals obtained from the structure are modeled as autoregressive moving average (ARMA) processes. The feature vector used consists of the Frst three autoregressive coefFcients obtained from the modeling of the vibration signals. Damage is detected by observing a migration of the extracted AR coefFcients with dam- age. A Gaussian Mixture Model (GMM) is used to model the feature vector. Damage is detected using the gap statistic, which ascertains the optimal number of mixtures in a particular dataset. The Mahalanobis distance between the mixture in question and the baseline (undamaged) mixture is a good indicator of damage extent. Application cases from the ASCE Benchmark Structure simulated data have been used to test the efFcacy of the algorithm. This approach provides a useful framework for data fusion, where different measurements such as strains, temperature, and humidity could be used for a more robust damage decision. f DOI: 10.1115/1.2718241 g Keywords: structural health monitoring, damage diagnosis, pattern classiFcation, Gaussian mixture models 1 Introduction Structural health monitoring s SHM d has received increasing at- tention in the research community f 1 g . Recent research has also demonstrated that wireless sensing networks can be successfully used for structural health monitoring f 2,3 g . Low cost microeletro- mechanical s MEMS d sensors and wireless solutions have been fabricated for structural measurement and this allows for a dense network of sensors to be deployed in structures. A key require- ment in these networks is a low data transmission rate. To achieve low transmission rates, computational processing capabilities are being incorporated at the sensing nodes to enable local data inter- rogation and analysis. Thus, results of the data analysis are only transmitted resulting in signiFcant data compression. Damage detection is based on the premise that damage in the structure will cause changes in vibration data. Vibration based methods are divided into model based and nonmodel based meth- ods f 4 g . Model based methods give a quantitative assessment of damage. However, these are computationally expensive and re- quire a Fnite element model, which has to be suitably updated at each stage of damage. Nonmodel based methods are not as com- putational intensive, but do not give a quantitative assessment.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 9

time serie STR DID math algorithm - K. Krishnan Nair Ph.D....

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online