Filipe Magalhães

Combined Experimental-Operational Modal Testing of Footbridges

In combined vibration testing, an artificial, measured force is used in operational conditions. This requires the identification of a system model that takes both the measured and the operational excitation into account. Advantages with respect to the classical operational modal analysis approach are the possibility of obtaining mass-normalized mode shapes and the increase of the excitation level and its frequency content. An advantage with respect to the classical experimental modal analysis approach, where the ambient excitation is not modeled, but considered as disturbing noise, is the possibility of using excitation levels that are of the same amplitude, or even smaller, than the ambient excitation levels. In this paper, combined modal testing of footbridges is explored using two case studies: a steel arch footbridge with spans of 75.2 m and 30.3 m and a concrete stress-ribbon footbridge with spans of 30 m and 28 m. The comparison of the modal parameters (eigenfrequencies, damping ratios, mode shapes,...

Active illumination single-pixel camera based on compressive sensing

We present an optical imaging system based on compressive sensing (CS) along with its principal mathematical aspects. Although CS is undergoing significant advances and empowering many discussions and applications throughout various fields, this article focuses on the analysis of a single-pixel camera. This work was the core for the development of a single-pixel camera approach based on active illumination. Therefore, the active illumination concept is described along with the experimental results, which were very encouraging toward the development of compressive-sensing-based cameras for various applications, such as pixel-level programmable gain imaging.

Structural health monitoring of offshore wind turbines using automated operational modal analysis

This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.

High-resolution hyperspectral single-pixel imaging system based on compressive sensing

For the first time, a high-resolution hyperspectral single-pixel imaging system based on compressive sensing is presented and demonstrated. The system integrates a digital micro-mirror device array to optically compress the image to be acquired and an optical spectrum analyzer to enable high spectral resolution. The systems ability to successfully reconstruct images with 10 pm spectral resolution is proven.

On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridge

Structural health monitoring allows the automated condition assessment of civil infrastructure, leading to a cost-effective management of maintenance activities. However, there is still a debate in the literature about the effectiveness of available signal processing strategies to timely assess the health state of a structure. This paper is a contribution to this debate, by presenting the application of different vibration-based damage detection methods using up-to-date multivariate statistical analysis techniques applied to data acquired from a permanently monitored long-span arch bridge. Techniques based on dynamic regression models, linear and local principal component analysis, as well as on their combinations, including, in particular, the newly proposed method based on the combination of dynamic multiple linear regressions and local principal component analysis, and, finally, a method based on the recently proposed approach of cointegration, are considered. A first effort is made to formulate these methods within a unique mathematical framework, highlighting, in particular, the relevant parameters affecting their results and proposing objective criteria for their appropriate tuning and for choosing the length of the training period. Then, the considered damage detection methods are implemented and applied to field data, seeking for damage-sensitive features in the presence of variable environmental and operational conditions. The considered techniques are applied to time histories of identified modal frequencies of the bridge and their capability to reveal structural damage of varying severity is assessed using control charts. The case of an artificially imposed non-linear correlation between the features is also considered. The results provide, for the first time in the literature, an estimation of the minimum level of damage that can be realistically detected in the bridge using dynamic signatures and up-to-date signal processing algorithms, thus contributing to a more aware use of monitoring data and reliance over related health state assessment information.

LabVIEW toolkits for output-only modal identification and long-term dynamic structural monitoring

This paper describes the development of computational tools for modal identification and long term dynamic monitoring in LabVIEW environment. These tools mainly consist of two individual toolkits for structural modal identification (SMI) and continuous monitoring (CSMI), respectively. The SMI toolkit implements the frequency domain Peak-Picking (PP) and Enhanced Frequency Domain Decomposition (EFDD) method, as well as the time domain Stochastic Subspace Identification (SSI) techniques. Based on this toolkit, the user can easily develop the whole process of structural modal identification by simply pushing buttons. The CSMI toolkit was conceived for continuous dynamic monitoring excluding manual interaction. It automatically searches the latest output measurements, detects maximum vibration amplitudes and makes statistical treatment of acceleration time series, generates waterfall plots for depicting the frequency component distribution and identifies modal parameters based on automated EFDD technique. The application of these tools is briefly described based on experimental data collected at Pinhão bridge and Coimbra footbridge.

High dynamic range compressive imaging: a programmable imaging system

Some scenes and objects have a wide range of brightness that cannot be captured with a conventional camera. This limitation, which degrades the dynamic range of an imaged scene or object, is addressed by use of high dynamic range (HDR) imaging techniques. With HDR ima- ging techniques, images of a very broad range of intensity can be obtained with conventional cameras. Another limitation of conventional cameras is the range of wavelength that they can capture. Outside the visible wave- lengths, the responsivity of conventional cameras drops; therefore, a con- ventional camera cannot capture images in nonvisible wavelengths. Compressive imaging is a solution for this problem. Compressive imaging reduces the number of pixels of a camera to one, so a camera can be replaced by a detector with one pixel. The range of wavelengths to which such detectors are responsive is much wider than that of a conven- tional camera. A combination of HDR imaging and compressive imaging is introduced and is benefitted from the advantages of both techniques. An algorithm that combines these two techniques is proposed, and results are presented.

A new method for the detection of singular points in fingerprint images

Automatic biometric identification based on fingerprints is still one of the most reliable identification method in criminal and forensic applications. A critical step in fingerprint analysis without human intervention is to automatically and reliably extract singular points from the input fingerprint images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. Poincare¿ Index-based methods are one of the most common for singular points detection. However, these methods usually result in many spurious detections. Therefore, we propose an enhanced version of the method presented by Zhou et al. [13] that introduced a feature called DORIC to improve the detection. Our principal contribution lies in the adoption of a smoothed orientation field and in the formulation of a new algorithm to analyze the DORIC feature. Experimental results show that the proposed algorithm is accurate and robust, giving better results than the best reported results so far, with improvements in the range of 5% to 7%.

Pre- and Post-identification Merging for Multi-Setup OMA with Covariance-Driven SSI

In Operational Modal Analysis (OMA) of large structures we often need to process sensor data from multiple nonsimultaneously recorded measurement setups. These setups share some sensors in common, the so-called reference sensors that are fixed for all the measurements, while the other sensors are moved from one setup to the next. To obtain the modal parameters of the investigated structure, it is necessary to process the data of all the measurement setups and normalize it as the unmeasured background excitation of each setup might be different. For this we compare three different approaches in this paper which differ in the order of the data merging, normalization and system identification step: The classical PoSER (identification-normalization-merging), the PoGER (merging-identification-normalization) and the PreGER (normalization-merging-identification). Special care was taken with the PreGER method and its efficiency has been tested with respect to the two other methods. The system identification is done with the SSI-cov/ref method. We apply these methods to the extraction of the modal parameters (natural frequencies, damping ratios and mode shapes) of the Luiz I arch bridge in Porto, Portugal, compare them and evaluate the different methods.

Evaluation of dynamic effects and fatigue assessment of a metallic railway bridge

In the framework of the national research project ‘Evaluation of the structural integrity of metallic railway bridges’, the Laboratory of Vibrations and Monitoring of the Faculty of Engineering of the University of Porto, Portugal has recently developed a study concerning the assessment of the dynamic behaviour and fatigue condition of a metallic railway bridge on the international ‘Beira Alta’ route in Portugal. This paper describes the numerical simulation of the dynamic behaviour of the bridge under railway traffic, the experimental updating and validation of the finite-element model used, and the estimation of the fatigue residual life using different code specifications and the characteristics of real trains crossing over the bridge.