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: The use of non-destructive assessment techniques for evaluating structural conditions of aging infrastructure, such as timber bridges, utility poles and buildings, for the past 20 years has faced increasing challenges as a result of poor maintenance and inadequate funding. Replacement of structures, such as an old bridge, is neither viable nor sustainable in many circumstances. Hence, there is an urgent need to develop and utilize state-of-the-art techniques to assess and evaluate the " health state " of existing infrastructure and to be able to understand and quantify the effects of degradation with regard to public safety. This paper presents an overview of research work carried out by the authors in developing and implementing several vibration methods for evaluation of damage in timber bridges and utility poles. The technique of detecting damage involved the use of vibration methods, namely damage index method, which also incorporated artificial neural networks for timber bridges and time-based non-destructive evaluation (NDE) methods for timber utility poles. The projects involved successful numerical modeling and good experimental validation for the proposed vibration methods to detect damage for simple beams subjected to single and multiple damage scenarios and was then extended to a scaled timber bridge constructed under laboratory conditions. The time-based NDE methods also showed promising trends for detecting the embedded depth and condition of timber utility poles in early stages of that research.Structural Health Monitoring in Australia, Edited by Tommy Chan, David Thambiratnam, 01/2011: pages 81-108; Nova Science Publishers Inc..
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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. · 0.60 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. · 0.60 Impact Factor