Frequency response based damage detection using principal component analysis
ABSTRACT In this paper we explore structural damage detection using frequency response signals and principal component analysis. While frequency responses are easy to measure especially in online damage detection applications, most of the associated detection methods are deterministic in nature and cannot deal with uncertainties and noise which are inevitable under practical situations. To tackle this issue and to develop a robust damage detection protocol, here we develop a feature extraction/de-noising methodology based on principal component analysis (PCA). The basic idea is to first establish a feature space of the intact structure response by using multiple measurements. Abnormal signature that is different from the baseline signature can then be identified and magnified after signal reconstruction using the intact structure features. Essentially, the directionality between an inspected signal and the baseline signal in the feature space is used as index of damage occurrence. A series of numerical analyses are performed to characterize the detection system sensitivity and robustness.
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ABSTRACT: This paper presents a novel vibration-based technique that utilises changes in frequency response functions (FRFs) to assess advancement of damage in timber bridges. In the proposed method, damage patterns embedded in FRF data are extracted and analysed by using a combination of principal component analysis (PCA) and artificial neural network (ANN) techniques for estimation of severity levels of damage. To demonstrate the method, it is applied to a laboratory four-girder timber bridge, which is gradually inflicted with accumulative damage at different locations and severities. To extract damage features in FRFs and to compress the large size of FRF data, FRFs are transferred to the principal component space adopting PCA techniques. PCA-compressed FRF data are then used as inputs to ANNs to identify severities of damage. The excellent severity predictions obtained from the ANNs show that FRF data can potentially be very good indicators for the assessment of damage advancements in timber bridges.International Conference on Structural Health Assessment of Timber Structures, Lisbon, Portugal; 06/2011
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ABSTRACT: This paper presents a vibration-based damage identification method that utilises damage fingerprints embedded in frequency response functions (FRFs) to identify location and severity of notch-type damage in a two-storey framed structure. The proposed method utilises artificial neural networks (ANNs) to map changes in FRFs to damage characteristics. To enhance damage fingerprints in FRF data, residual FRFs, which are differences in FRF data between the undamaged and the damaged structures, are used for ANN inputs. By adopting principal component analysis (PCA) techniques, the size of the residual FRF data is reduced in order to obtain suitable patterns for ANN inputs. A hierarchy of neural network ensembles is created to take advantage of individual characteristics of measurements from different locations. The method is applied to laboratory and numerical two-storey framed structures. A number of single notch-type damage scenarios of different locations and severities are investigated. To simulate field-testing conditions, numerically simulated data is polluted with white Gaussian noise of up to 10% noise-to-signal-ratio. The results from both numerical and experimental investigations show the proposed method is effective and robust for detecting notch-type damage in structures.Advances in Structural Engineering 01/2012; 15(5):743-757. · 0.49 Impact Factor
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ABSTRACT: This paper presents a vibration-based damage identification method that utilises a "damage fingerprint" of a structure in combination with Principal Component Analysis (PCA) and neural network techniques to identify defects. The Damage Index (DI) method is used to extract unique damage patterns from a damaged beam structure with the undamaged structure as baseline. PCA is applied to reduce the effect of measurement noise and optimise neural network training. PCA-compressed DI values are, then, used as inputs for a hierarchy of neural network ensembles to estimate locations and severities of various damage cases. The developed method is verified by a laboratory structure and numerical simulations in which measurement noise is taken into account with different levels of white Gaussian noise added. The damage identification results obtained from the neural network ensembles show that the presented method is capable of overcoming problems inherent in the conventional DI method. Issues associated with field testing conditions are successfully dealt with for numerical and the experimental simulations. Moreover, it is shown that the neural network ensemble produces results that are more accurate than any of the outcomes of the individual neural networks.Advances in Structural Engineering 01/2010; 13(6):1001-1016. · 0.49 Impact Factor