Condition-based maintenance refers to the installation of permanent sensors on a structure/system. By means of early fault detection, severe damage can be avoided, allowing efficient timing of maintenance works and avoiding unnecessary inspections at the same time. These are the goals for structural health monitoring (SHM). The changes caused by incipient damage on raw data collected by sensors are quite small, and are usually contaminated by noise and varying environmental factors, so the algorithms used to extract information from sensor data need to focus on sensitive damage features. The developments of SHM techniques over the last 20 years have been more related to algorithm improvements than to sensor progress, which essentially have been maintained without major conceptual changes (with regards to accelerometers, piezoelectric wafers, and fiber optic sensors). The main different SHM systems (vibration methods, strain-based fiber optics methods, guided waves, acoustic emission, and nanoparticle-doped resins) are reviewed, and the main issues to be solved are identified. Reliability is the key question, and can only be demonstrated through a probability of detection (POD) analysis. Attention has only been paid to this issue over the last ten years, but now it is a growing trend. Simulation of the SHM system is needed in order to reduce the number of experiments.

No abstract

Fiber-optic sensors cannot measure damage; to get information about damage from strain measurements, additional strategies are needed, and several alternatives are available in the existing literature. This paper discusses two independent procedures. The first is based on detecting new strains appearing around a damage spot. The structure does not need to be under loads, the technique is very robust, and damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers wider coverage of the structure; it is based on identifying the changes caused by damage on the strain field in the whole structure for similar external loads. Damage location does not need to be known a priori, and detectability is dependent upon the sensor’s network density, the damage size, and the external loads. Examples of application to real structures are given.

A novel methodology for damage detection and location in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PC A (principal component analysis) and damage indices (T^ and Q). We propose the use of fiber Bragg gratings (FBGs) as strain sensors.

In this paper, a novel methodology for damage localization is introduced. The approach is based on a multiactuator system. This means that the system itself has the ability of both exciting the specimen and measuring its response at different points in a pitch-catch mode. Once one of its actuators excites the specimen, the damage affects the normal travel of the guided wave, and this change is mainly detected by sensors in the direct route to the excitation point. In previous works by the authors, it can been observed the progression using data-driven statistical models (multivariable analysis based on principal component analysis, PCA) of all recorded signals to determine whether the damage is present. However, the main contribution of this paper is the demonstration of the possibility of localizing damages by analyzing the contribution of each sensor to this index which have detected it (T 2-statistic and Q-statistic). The proposed methodology has been applied and validated on an aircraft turbine blade. The results indicate that the presented methodology is able to accurately locate damages, analyzing the record signals from all actuation phases, giving a unique and reliable region.

In this paper, a design method for a photovoltaic system based on a dual active bridge converter and a photovoltaic module is proposed. The method is supported by analytical results and theoretical predictions, which are confirmed with circuital simulations. The analytical development, the theoretical predictions, and the validation through circuital simulations, are the main contributions of the paper. The dual active bridge converter is selected due to its high efficiency, high input and output voltages range, and high voltage-conversion ratio, which enables the interface of low-voltage photovoltaic modules with a high-voltage dc bus, such as the input of a micro-inverter. To propose the design method, the circuital analysis of the dual active bridge converter is performed to describe the general waveforms derived from the circuit behavior. Then, the analysis of the dual active bridge converter, interacting with a photovoltaic module driven by a maximum power point tracking algorithm, is used to establish the mathematical expressions for the leakage inductor current, the photovoltaic current, and the range of operation for the phase shift. The design method also provides analytical equations for both the high-frequency transformer equivalent leakage inductor and the photovoltaic side capacitor. The design method is validated through detailed circuital simulations of the whole photovoltaic system, which confirm that the maximum power of the photovoltaic module can be extracted with a correct design of the dual active bridge converter. Also, the theoretical restrictions of the photovoltaic system, such as the photovoltaic voltage and power ripples, are fulfilled with errors lower than 2% with respect to the circuital simulations. Finally, the simulation results also demonstrate that the maximum power point for different environmental conditions is reached, optimizing the phase shift factor with a maximum power point tracking algorithm.

The use of fossil resources for electricity production is one of the primary reasons for increasing greenhouse emissions and is a non-renewable resource. Therefore, the electricity generation by wind and solar resources have had greater applicability in recent years. Hybrid Renewable Energy Systems (HRES) integrates renewable sources and storage systems, increasing the reliability of generators. For the sizing of HRES, Artificial Intelligence (AI) methods such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) stand out. This article presents the sizing of an HRES for the Colombian context, taking into account the energy consumption by three typical demands, four types of wind turbines, three types of solar panels, and a storage system for the system configuration. Two optimization approaches were set-up with both optimization strategies (i.e., GA and PSO). The first one implies the minimization of the Loss Power Supply Probability (LPSP). In contrast, the second one concerns adding the Total Annual Cost (TAC) or the Levelized Cost of Energy (LCOE) to the objective function. Results obtained show that HRES can supply the energy demand, where the PSO method gives configurations that are more adjusted to the considered electricity demands.

We report the first experimental demonstration of a humidity insensitive polymer optical fiber Bragg grating (FBG), as well as the first FBG recorded in a TOPAS polymer optical fiber in the important low loss 850nm spectral region. For the demonstration we have fabricated FBGs with resonance wavelength around 850 nm and 1550 nm in single-mode microstructured polymer optical fibers made of TOPAS and the conventional poly (methyl methacrylate) (PMMA). Characterization of the FBGs shows that the TOPAS FBG is more than 50 times less sensitive to humidity than the conventional PMMA FBG in both wavelength regimes. This makes the TOPAS FBG very appealing for sensing applications as it appears to solve the humidity sensitivity problem suffered by the PMMA FBG.