Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures.

Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model.

Axially loaded beam-like structures represent a challenging case study for unsupervised learning vibration-based damage detection. Under real environmental and operational conditions, changes in axial load cause changes in the characteristics of the dynamic response that are significantly greater than those due to damage at an early stage. In previous works, the authors proposed the adoption of a multivariate damage feature composed of eigenfrequencies of multiple vibration modes. Successful results were obtained by framing the problem of damage detection as that of unsupervised outlier detection, adopting the well-known Mahalanobis squared distance (MSD) to define an effective damage index. Starting from these promising results, a novel approach based on unsupervised learning data clustering is proposed in this work, which increases the sensitivity to damage and significantly reduces the uncertainty associated with the results, allowing for earlier damage detection. The novel approach, which is based on Gaussian mixture model, is compared with the benchmark one based on the MSD, under the effects of an uncontrolled environment and, most importantly, in the presence of real damage due to corrosion.

Automatic Detection of Real Damage in Operating Tie-Rods.

Many researchers have proposed vibration-based damage-detection approaches for continuous structural health monitoring. Translation to real applications is not always straightforward because the proposed methods have mostly been developed and validated in controlled environments, and they have not proven to be effective in detecting real damage when considering real scenarios in which environmental and operational variations are not controlled. This work was aimed to develop a fully-automated strategy to detect damage in operating tie-rods that only requires one sensor and that can be carried out without knowledge of physical variables, e.g., the axial load. This strategy was created by defining a damage feature based on tie-rod eigenfrequencies and developing a data-cleansing strategy that could significantly improve performance of outlier detection based on the Mahalanobis squared distance in real applications. Additionally, the majority of damage-detection algorithms presented in the literature related to structural health monitoring were validated in controlled environments considering simulated damage conditions. On the contrary, the approach proposed in this paper was shown to allow for the early detection of real damage associated with a corrosion attack under the effects of an intentionally uncontrolled environment.

Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures.

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements.

Fiber-optic combined FPI/FBG sensors for monitoring of radiofrequency thermal ablation of liver tumors: ex vivo experiments.

We present a biocompatible, all-glass, 0.2 mm diameter, fiber-optic probe that combines an extrinsic Fabry-Perot interferometry and a proximal fiber Bragg grating sensor; the probe enables dual pressure and temperature measurement on an active 4 mm length, with 40 Pa and 0.2°C nominal accuracy. The sensing system has been applied to monitor online the radiofrequency thermal ablation of tumors in liver tissue. Preliminary experiments have been performed in a reference chamber with uniform heating; further experiments have been carried out on ex vivo porcine liver, which allowed the measurement of a steep temperature gradient and monitoring of the local pressure increase during the ablation procedure.

Moving microphone arrays to reduce spatial aliasing in the beamforming technique: theoretical background and numerical investigation.

This paper introduces a measurement technique aimed at reducing or possibly eliminating the spatial aliasing problem in the beamforming technique. Beamforming main disadvantages are a poor spatial resolution, at low frequency, and the spatial aliasing problem, at higher frequency, leading to the identification of false sources. The idea is to move the microphone array during the measurement operation. In this paper, the proposed approach is theoretically and numerically investigated by means of simple sound propagation models, proving its efficiency in reducing the spatial aliasing. A number of different array configurations are numerically investigated together with the most important parameters governing this measurement technique. A set of numerical results concerning the case of a planar rotating array is shown, together with a first experimental validation of the method.

Optical fiber sensors-based temperature distribution measurement in ex vivo radiofrequency ablation with submillimeter resolution.

Radiofrequency thermal ablation (RFTA) induces a high-temperature field in a biological tissue having steep spatial (up to 6°C∕mm) and temporal (up to 1°C∕s) gradients. Applied in cancer care, RFTA produces a localized heating, cytotoxic for tumor cells, and is able to treat tumors with sizes up to 3 to 5 cm in diameter. The online measurement of temperature distribution at the RFTA point of care has been previously carried out with miniature thermocouples and optical fiber sensors, which exhibit problems of size, alteration of RFTA pattern, hysteresis, and sensor density worse than 1 sensor∕cm. In this work, we apply a distributed temperature sensor (DTS) with a submillimeter spatial resolution for the monitoring of RFTA in porcine liver tissue. The DTS demodulates the chaotic Rayleigh backscattering pattern with an interferometric setup to obtain the real-time temperature distribution. A measurement chamber has been set up with the fiber crossing the tissue along different diameters. Several experiments have been carried out measuring the space-time evolution of temperature during RFTA. The present work showcases the temperature monitoring in RFTA with an unprecedented spatial resolution and is exportable to in vivo measurement; the acquired data can be particularly useful for the validation of RFTA computational models.

Fiber-optic chirped FBG for distributed thermal monitoring of ex-vivo radiofrequency ablation of liver.

A linearly chirped fiber Bragg grating (LCFBG) has been used as a temperature sensor for online monitoring of radiofrequency thermal ablation (RFTA). The LCFBG acts as a distributed sensor, with spatial resolution of 75 µm. A white-light setup that records the LCFBG spectrum estimates the temperature profile in real time. Three RFTA experiments have been performed ex-vivo on porcine liver measuring the radial temperature distribution during the heating process. The analysis of thermal maps quantifies the spatial heat distribution along the measurement axis and determines the ablation efficiency.

Applicability of cranial models in urethane resin and foam as a substitute for bone: are synthetic materials reliable?

As literature is poor in functional synthetic cranial models, in this study, synthetic handmade models of cranial vaults were produced in two different materials (a urethane resin and a self-hardening foam), from multiple bone specimens (eight original cranial vaults: four human and four swine), in order to test their resemblance to bone structure in behavior, during fracture formation. All the vaults were mechanically tested with a 2-kg impact weight and filmed with a high-speed camera. Fracture patterns were homogeneous in all swine vaults and heterogeneous in human vaults, with resin fractures more similar to bone fractures. Mean fracture latency time extrapolated by videos were of 0.75 msec (bone), 1.5 msec (resin), 5.12 msec (foam) for human vaults and of 0.625 msec (bone), 1.87 msec (resin), 3.75 msec (foam) for swine vaults. These data showed that resin models are more similar to bone than foam reproductions, but that synthetic material may behave quite differently from bone as concerns fracture latency times.

Personal identification by the comparison of facial profiles: testing the reliability of a high-resolution 3D-2D comparison model.

Identification from video surveillance systems is frequently requested in forensic practice. The "3D-2D" comparison has proven to be reliable in assessing identification but still requires standardization; this study concerns the validation of the 3D-2D profile comparison. The 3D models of the faces of five individuals were compared with photographs from the same subjects as well as from another 45 individuals. The difference in area and distance between maxima (glabella, tip of nose, fore point of upper and lower lips, pogonion) and minima points (selion, subnasale, stomion, suprapogonion) were measured. The highest difference in area between the 3D model and the 2D image was between 43 and 133 mm(2) in the five matches, always greater than 157 mm(2) in mismatches; the mean distance between the points was greater than 1.96 mm in mismatches, <1.9 mm in five matches (p < 0.05). These results indicate that this difference in areas may point toward a manner of distinguishing "correct" from "incorrect" matches.