Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Silva, Samuel da; Júnior, Milton Dias; Lopes, Vicente

doi:10.1590/s1678-58782007000200007

Cited by 36 publications

(22 citation statements)

References 14 publications

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“…To extract feature vectors for a local area, time series measurement data sets from multiple sensors are reduced to lower dimensions by the Principal Component Analysis (PCA) method. The PCA is a statistical technique that uses a substantially smaller set of uncorrelated variables to represent the maximum amount of information from the original set of variables [8]. The PCA method involves the calculation of the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute.…”

Section: Notational Conventionmentioning

confidence: 99%

“…Modal parameters, such as natural frequencies, damping ratios, and mode shape curvature, have been the primary features used to identify damage in structures. Recently, a number of new approaches, such as statistical pattern recognition [7,8] and neural network [9][10][11], have been proposed for the damage diagnosis. For example, Sohn and Farrar [7] proposed a statistical pattern 0045 recognition method for the damage diagnosis using time series analysis of vibration signals.…”

Section: Introductionmentioning

confidence: 99%

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Artificial immune pattern recognition for structure damage classification

Chen

Zang

2009

Computers & Structures

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Section: Notational Conventionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial immune pattern recognition for structure damage classification

Chen

Zang

2009

Computers & Structures

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“…Amir et al [28] used the ARV models and a statistical measure to identify the damage locations. Silva et al [29] used the classical fuzzy c-means algorithm to classify the level of damage in a structure based on AR -ARX prediction error using three different clusters (healthy, damaged and severely damaged states) based on the American Society of Civil Engineers (ASCE) benchmark.…”

Section: Introductionmentioning

confidence: 99%

A robust damage-detection technique with environmental variability combining time-series models with principal components

Lakshmi

Rao

2014

Nondestructive Testing and Evaluation

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In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration timehistory data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

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“…Modal parameters, such as natural frequencies, damping ratios, and mode shape curvature, have been the primary features used to identify damage in structures. Recently, a number of new approaches, such as statistical pattern recognition [1][2][3], neural network [4][5][6], and bio-inspired pattern recognition [7,8], have been proposed for structural damage diagnosis. Due to high dimension of time-series data, direct processing and storage of time-series data in its raw format is expensive.…”

Section: Introductionmentioning

confidence: 99%

Comparison study of feature extraction methods in structural damage pattern recognition

2011

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This paper compares the performance of various feature extraction methods applied to structural sensor measurements acquired in-situ, from a decommissioned bridge under realistic damage scenarios. Three feature extraction methods are applied to sensor data to generate feature vectors for normal and damaged structure data patterns. The investigated feature extraction methods include identification of both time domain methods as well as frequency domain methods. The evaluation of the feature extraction methods is performed by examining distance values among different patterns, distance values among feature vectors in the same pattern, and pattern recognition success rate. The test data used in the comparison study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete boxgirder bridge under progressively increasing damage scenarios. A number of progressive damage test case data sets, including undamaged cases and pier settlement cases (different depths), are used to test the separation of feature vectors among different patterns and the pattern recognition success rate for different feature extraction methods is reported.

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Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Abstract: Structural health monitoring (SHM) is

Cited by 36 publications

References 14 publications

Artificial immune pattern recognition for structure damage classification

Artificial immune pattern recognition for structure damage classification

A robust damage-detection technique with environmental variability combining time-series models with principal components

Comparison study of feature extraction methods in structural damage pattern recognition

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