Abstract: Carbon fiber-reinforced polymer (CFRP) composites are lightweight, durable, and corrosionresistive materials that are popular for constructing automotive bodies and aircraft structures. However, their heterogeneous composition and anisotropic mechanical behavior make design of their service lives challenging. To address challenges in monitoring CFRP’s structural behavior, a cheap, weightless, and reliable sensor shall be developed for CFRPs to monitor their damage-to-failure mode. In this study a carbon nanotube (CNT)-embedded thin film is inkjet-printed onto a flexible substrate and applied over a tensile testing coupon. Coupled with the algorithm of electrical impedance tomography, the sensor with 16 electrodes is able to reconstruct the strain distribution of a surface under 8 sec. Non-uniform strain distribution can also be reconstructed at strain levels down to 0.001%.
Abstract: The paper aims to discuss the basic issues related to the analysis and design of magnetic sensors based on the employment of magneto-active materials. In particular, the basic idea is based on the integration of a Galfenol magnetostrictive alloy to a Fiber Bragg Grating (FBG) embedded into an optic fiber, able to sense the deformation of the material induced by magnetic field. The structure of the alloy and the characteristics of the fiber, make the device suitable to work also in harsh envi- ronments. One of the basic goals is to provide a sensor as simple as possible, with high field range detection and, at the same time, low reconstruction error. It has been observed that the increase of the field range could be achieved by exploiting the effects of the demagnetizing field, without exploit- ing the well-known magnetic hardening induced by the applied stress. In fact, the latter requires a clamping system, resulting in the increase of the sensor size. The demagnetizing field, conversely, provides a shielding of the external field, turning away the undesired approach to saturation. Finally, the employment of a material characterized by weak hysteresis phenomena avoids the use of complex compensation algorithm without losing accuracy. Some result of its characteristics and performances are provided.
Abstract: The concept of structural health monitoring (SHM) has appealed the attentions of structural engineers. However, most of the proposed schemes for SHM do not seem “friendly” to the practicing engineers in terms of the used data or employed methods. In this regard, the direct sensing of inter-story drift displacements could open the door to the construction of “practicing engineers friendly” SHM schemes. The authors‘ group developed non-contact types of inter-story drift displacement sensors. Several schemes based on the drift displacement sensing are discussed, which do not involve heavy researchers-oriented processes.
Abstract: Energy harvesting is a solution to feed wireless sensors for bridge structural health monitoring. Indeed, vibrations induced by traffic passing can be converted into electrical energy with suitable devices. This paper presents laboratory tests over a device based on galfenol rods, a magnetostrictive material. It is presented the general design and both mechanical and magneto-mechanical tests to verify the performance.
Abstract: The presence of complex boundary conditions makes the estimation of cable forces in cable-stayed bridges quite difficult when using conventional model-based force identification methodologies. A large dataset of recorded acceleration signals is available for the Ting Kau Bridge (TKB) in Hong Kong. The dataset is used together with a numerical model of the bridge to reconstruct the tension forces in the main cables. A part of the data is used to calibrate the model. The remaining data are used for validation. The created numerical model permits an investigation of the tensions distribution in the stay-cables during a typhoon, based on the observed increase of some of the bridge frequencies during this extreme event.
Abstract: Load monitoring and damage identification are important tasks in the field of Structural Health Monitoring and are necessary for assessing the structural integrity and predicting the remaining useful life time. Reconstructing unknown force inputs or system parameters usually involves the solution of an inverse problem which is mostly ill-posed and therefore needs regularization. Using prior information about the desired values is advisable for obtaining meaningful solutions. Damages like for example cracks can often be interpreted as spatial singularities, which cause local stiffness reductions of the observed structures. Damage identification is the task of localizingand quantifying these stiffness reductions. On the other hand, unknown structure excitation usually has also some specia lcharacteristics which can be assumed as known apriori, e.g. spatial concentration for singular forces, short time duration for impact loads or narrow frequency bands for harmonic loads. In this case force reconstruction becomes also a localization and magnitude estimation problem. Thischaracteristic information is used to transform the inverse problem into a sparse recovery task. Inthe last years sparsity constrained regularization of inverse problem has attracted a lot of attention inapplied mathematics, especially in the context of compressive sensing.In this contribution it is shown how sparse solution techniques can be applied in monitoring sys-tems and how this will improve the reconstruction results and additionally reduce the number of required sensors.
Abstract: This work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some fault. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large wind turbine in the presence of wind turbulence and realistic fault scenarios.
Abstract: Data evaluation is crucial for gaining information from sensor networks. Main challenges include processing speed and adaptivity to system change, both prerequisites for SHM-based weight reduction via relaxed safety factors. Our study looks at soft real time solutions providing feedback within defined but flexible, application-controlled intervals. These can rely on minimizing computation/communication latencies e.g. by parallel computation. Strategies towards this aim can be model-based, including inverse FEM, or model-free, including machine learning, which in practice bases training on a defined system state, too, hence also facing challenges at state changes. We thus introduce hybrid data evaluation combining multi-agent based systems (MAS) with inverse FEM, mainly relying on matrix operations that can be partially distributed: The MAS perform sensor data acquisition, aggregation, pre-computation, and finally application (the LM/SHM itself and higher information processing and visualization layers, i.e., WEB interfaces). System capabilities are evaluated against a virtual test case, demonstrating enhanced stability and reliability. Besides, we analyze system performance under conditions of in-service change and discuss system layouts suited to improve coverage of this issue.
Abstract: The ability of a material to recover its nominal properties through self-healing is gaininginterest in the research community. However, current approaches remain predominantly passive incounteracting the effect of damage. As a result, healing only begins when the material has occurreddamage and typically there is a mismatch between the healing and damage rate. For applications suchas aircraft, where there is a thin line between functionality and non-functionality, these limitations maybe inherently restrictive. A self-healing system that combines a prognosis unit to predict and estimatethe failure rate and an active self-healing system that matches the healing rate to the estimated failurerate using a feedback loop, has the potential to overcome these limitations. In this paper we proposesuch a system and present results for its application to composite materials.