El grup fa recerca interdisciplinària en àrees com la matemàtica aplicada, la teoria de sistemes i l'enginyeria del control. La recerca progressa en dues direccions interrelacionades: una teòrica, aportant coneixements a l'estat de l'art; i una aplicada, desenvolupant eines d'utilitat en sistemes del món real, particularment en sistemes mecànics, de l'enginyeria civil i, recentment, de l'àmbit biomèdic.
The use of guided wave-based approaches presents some advantages in the structural inspection and damage identification processes. It is driven by the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects, however, its use in Structural Health Monitoring requires the development of efficient SHM methodologies to analyse and provide confident results. To do that, signal processing techniques for the correct interpretation of the complex ultrasonic waves are a need. In this sense, it is necessary to still work on the
continuous search of methodologies for performing each one of the steps in the damage identification. As contribution, this paper presents a damage classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network. The document presents the description of the methodology including a description of the data reduction and the use of non-linear analysis of the information with hierarchical non-linear principal component analysis and some non-linear damage indices. The methodology is preliminary evaluated with a CFRP sandwich structure with some damages on the multi-layered composite sandwich structure which were intentionally produced to simulate different damage mechanisms, i.e. delamination and cracking of the skin. Finally, results are presented and discussed to remark the advantages and disadvantages of this methodology.
Unmanned aerial vehicles (UAVs) are well-known by its advantages in several applications such as surveillance and monitoring for instance in agricultural applications or fire control among others. These missions can be associated to the robotics area due to the smart applications and tasks that can be performed by these systems autonomously.
Although its designs and developments are in most of the cases joined to the applications, currently it is possible to design or acquire a UAV for specific applications by defining features about the task to perform. One of the problems with the use of UAV is attached to the variations of the operational conditions which can produce some damages during
operation, landing and de-landing tasks. Since several damages can affect the structural state of these vehicles, the use of a Structural Health Monitoring system is a necessity to provide an automatic monitoring system. This work includes a description of a preliminary
damage detection and classification system for a UAV. The system includes the description of the data analysis from a piezoelectric sensor network with independent component analysis and machine learning approaches. Some tests are available to validate the system with data from a wing of the UAV called VANT Solvendus from the
Fundación Universitaria Los Libertadores. Tests and the application of the methodology for detecting and classifying damage are performed to a part of the UAV wing skin and results show the advantage of the methodology.
Palacios-Quiñonero, F.; Rubió-Massegú, J.; Rossell, Josep M.; Reza, H. International Conference on Structural Dynamics p. 1749-1754 DOI: 10.1016/j.proeng.2017.09.433 Data de presentació: 2017-09 Presentació treball a congrés
In this paper, we present an advanced computational procedure that allows obtaining distributed energy-dissipation systems for large multi-story structures. The proposed methodology is based on a decentralized velocity-feedback energy-to-componentwise-peak (ECWP) controller design approach and can be formulated as a linear matrix inequality (LMI) optimization problem with structure constraints. To demonstrate the effectiveness of the proposed design methodology, a passive damping system is computed for the seismic protection of a 20-story building equipped with a complete set of interstory viscous dampers. The high-performance characteristics of the obtained passive ECWP control system are clearly evidenced by the numerical simulation results. Also, the computational effectiveness of the proposed design procedure is confirmed by the low computation time of the associated LMI optimization problem.
The modeling of magnetorheological dampers combines the use of the laws of physics along with
a phenomenological description. The phenomenological models that are used to describe these
devices present a symmetric hysteresis loop. However, the experimental hysteresis loops of MR
dampers are not symmetric. To take into account this asymmetry, we propose a modi cation of
the viscous + Dahl model, use the modi ed model to describe a large-scale magnetorheological
damper, and validate the modi ed model against experimental data.
Palacios-Quiñonero, F.; Rubió-Massegú, J.; Rossell, Josep M.; Karimi, H.R. World Congress of the International Federation of Automatic Control p. 13908-13913 DOI: doi.org/10.1016/j.ifacol.2017.08.2255 Data de presentació: 2017-07-13 Presentació treball a congrés
Inerters are a novel type of mechanical actuation devices that are able to produce large inertial forces with a relatively small mass. Due to this property, inerters can provide an effective solution to the main drawbacks of tuned mass-dampers and, consequently, they are gaining an increasing relevance in the field of passive structural vibration control. In this paper, a computational design strategy for inerter-based vibration control schemes is presented. The proposed approach combines a computationally effective reduced-frequency H8 cost-function and a constrained global optimization solver to design different configurations of a shared tuned mass-inerter-damper system for the seismic protection of a multi-story two-building structure. To assess the effectiveness of the obtained configurations, the frequency characteristics and the seismic response of the interstory drifts and interbuilding approaches are investigated with positive results.
Camacho-Navarro, J.; Ruiz, M.; Villamizar, R.; Mujica, L.E.; Moreno, G.; Quiroga , J. International Conference on Damage Assessment of Structures p. 1-9 Data de presentació: 2017-07-11 Presentació treball a congrés
Continuous monitoring for damage detection in structural assessment comprises implementation of low cost equipment and efficient algorithms. This work describes the stages involved in the design of a methodology with high feasibility to be used in continuous damage assessment. Specifically, an algorithm based on a data-driven approach by using principal component analysis and pre-processing acquired signals by means of cross-correlation functions, is discussed. A carbon steel pipe section and a laboratory tower were used as test structures in order to demonstrate the feasibility of the methodology to detect abrupt changes in the structural response when damages occur. Two types of damage cases are studied: crack and leak for each structure, respectively. Experimental results show that the methodology is promising in the continuous monitoring of real structures.
Quiroga , J.; Mujica, L.E.; Villamizar, R.; Ruiz, M.; Camacho-Navarro, J. International Conference on Damage Assessment of Structures p. 1-10 Data de presentació: 2017-07-11 Presentació treball a congrés
This paper presents the application of a Semi-Analytical Finite Element (SAFE) on determining the dispersion curves in homogeneous and isotropic plates subject to stress. Acoustoelasticity theory, dictates a stress dependence of the ultrasound bulk velocities since small non-linearities in the stress-strain relationships become significant changing the behavior of the lamb waves.
The effect of anisotropic stress on lamb waves produces anisotropy in the lamb wave dispersion curves affecting the phase and group velocities. Lately, a new approach has been used to face the dispersion curve generation in stressed specimens. In this new methodology, the isotropic specimen subjected to anisotropic loading is studied by proposing an equivalent stress-free anisotropic specimen. This approach, though is approximate, facilitates the determination of the dispersion curves treating the material as anisotropic and using the well studied numerical solution for the stress-free cases.
The lamb wave behavior in anisotropic materials can be studied via Effective Elastic Constants (EEC) reducing the complexity of the numerical implementation. Therefore, a numerical method combining the SAFE and EEC is presented as a tool for the dispersion curve generation in stressed plates. The validity and accuracy of this methodology is verified using previous results presented by others researches.
In previous works, damage detection of metallic specimens exposed to temperature changes has been achieved by using a statistical baseline model based on Principal Component Analysis (PCA), piezodiagnostics principle and taking into account temperature effect by augmenting the baseline model or by using several baseline models according to the current temperature. In this paper a new approach is presented, where damage detection is based in a new index that combine Q and T2 statistical indices with current temperature measurements.
Experimental tests were achieved in a 1m x 1” carbon-steel pipe section instrumented with piezodevices acting as actuators or sensors. A PCA baseline model was obtained to a temperature of 21º and then T2 and Q statistical indices were obtained for a 24h temperature profile. Also, mass adding at different points of pipe between sensor and actuator was used as damage. By using the combined index the temperature contribution can be separated and a better differentiation of damages respect to undamaged cases can be graphically obtained.
The algorithms commonly used for damage condition monitoring present several drawbacks related to unbalanced data, optimal training requirements, low capability to manage feature diversity and low tolerance to errors. In this work, an approach based on ensemble learning is discussed as alternative to obtain more efficient diagnosis. The main advantage of ensemble learning is the use of several algorithms at the same time for a better proficiency. Thereby, combining simplest tree decision algorithms in bagging scheme, the accuracy of damage detection is improved. It takes advantage by combining prediction of preliminary algorithms based on regression models. The methodology is experimentally validated on a carbon steel pipe section, where mass adding conditions are studied as possible failures. Data from an active system based on piezoelectric sensors are stored and characterized through the T2 and Q statistical indexes. Then, they are the inputs to the ensemble learning. The proposed methodology allows determining the condition assessment and damage localizations in the structure. The results of the studied cases show the feasibility of ensemble learning for detecting occurrence of structural damages with successful results.
The realization of adaptive-based controllers in many industrial control applications may exhibit the parametric drift behavior acquiring the well known bursting phenomenon. In this work, an original and novel technique is proposed to eliminate this phenomenon. It is based on a modification of the discrete-time delta modulator into its continuous-time domain and then by adding hysteresis. To verify our proposed method, numerical simulations are conducted to the Van der Pol oscillator control regulation problem solved by using a predictive adaptive-based control technique, where a small output time-delay is added to evidence the bursting phenomenon. Moreover, the realization of our hysteric delta modulator does not alter the original adaptive parametric process, this is an important contribution with respect to the existent algorithms on this topic.
Sanmiquel, L.; Solorzano, A.; Bascompta, M.; Rossell, Josep M.; Oliva, J.; Anticoi, H. World Congress on Mechanical, Chemical, and Material Engineering p. 140 DOI: 10.11159/mmme17.140 Data de presentació: 2017-06-08 Presentació treball a congrés
The InSAR technology has already been applied in ground movements, demonstrating its suitability for such purpose and managing huge quantities of data. The purpose of the study is to apply the SAR interferometry in a mining area and compare the outcomes with high precision data from Global Position System (GPS).
Thirteen satellite images have been processed for such purpose, covering a time range of five years (from 2007 to 2011), with the goal to know the subsidence during this period. The specific surface features of the zone have been taken into account. GPS data has also been processed and compared, obtaining the subsidence movement in the three directions and the mean subsidence velocity per year.
The comparison between both methods show similar results in terms of subsidence generated by the underground activity, validating the suitability of InSAR by means of GPS data. However, the existence of GPS measures is crucial to calibrate and keep the necessary accuracy of the system.
Moreno, G.; Villamizar, R.; Camacho-Navarro, J.; Ruiz, M.; Mujica, L.E. ECCOMAS Thematic Conference Smart Structures and Materials p. 1878-1889 Data de presentació: 2017-06-06 Presentació treball a congrés
Damage localization in structures can be achieved by using an appropriate data
interpretation algorithm based on the expected structural response. According to the several algorithms reported in literature, a different degree of accuracy is obtained according to complexity requirements. This paper presents a hybrid algorithm approach as alternative to combine some of the reported methods by employing an ensemble architecture. Thus, this damage assessment
algorithm integrates advantage of individual techniques in order to increase the performance of the whole expert system. The proposed architecture employs a network of piezoelectric devices to produce guided waves along the structure. The traveling of guided waves is affected by damage producing scattering, reflection and mode conversion, which can be characterized with statistical processing and pattern recognition methods. In this paper, supervised learning by means on ensemble learning, cross-correlation features, and PCA statistical indices are combined
for locating damages. An experimental validation is conducted on an aircraft turbine blade structure instrumented with an array of piezoelectric devices (PZT), where it is demonstrated the potential of the methodology to significantly enhance localization tasks.
Quiroga , J.; Mujica, L.E.; Villamizar, R.; Ruiz, M.; Camacho-Navarro, J. ECCOMAS Thematic Conference Smart Structures and Materials p. 1390-1397 Data de presentació: 2017-06-06 Presentació treball a congrés
This work provides a time domain approach to detect stress changes in hollow cylinders using low frequency ultrasound guided waves; the proposed scheme makes uses of the subtract method for stress level estimation. Residuals are generated by signal subtraction of the reference signal, at nominal condition, from the current signal. The stress condition is detected when the RMS of the residual goes above certain threshold. A simple supported hollow cylinder is utilized in this research and the different levels of stress, bending moment,
are obtained using a vertical variable force applied in the middle of the specimen; the induced stresses are in the elastic region of the stress-strain diagram. The guided waves are generated via a couple of piezoelectric transducers operating in a pitch-catch configuration to excite and detect longitudinal and flexural waves. A Matlab® script is utilized to perform ultrasonic signal generation, signal acquisition and processing. Results of each stress scenario demonstrates the feasibility and potential of using this formulation in the evaluation of the bending stress in hollow cylinders. The studied monitoring scheme can be used on-line and it is a cost-effective approach.
One of the goals of structural health monitoring (SHM) applications is to determine the presence and the severity of a damage. In some cases, this is an element to forecast the behaviour and take decisions to allocate maintenance or replace the structure or the piece. An appropriate decision can reduce the risk of an accident, making more efficient the management of maintenance tasks and reducing the costs while improving the performance of a system. In this way, the development of a good SHM system is a need. Through the use of: (i) advanced methodologies of digital signal processing; (ii) the acquisition of information from a set of piezoelectric sensors appropriately placed in the surface of a structure; and (iii) the use of techniques such as principal component analysis (PCA) [1, 2, 3] and
machine learning, it is possible to generate a solution that meets the necessity about the knowledge of
the structural state. This work presents a methodology which allow to determine the presence of a structural damage and its classification in spite of temperature changes. The methodology is tested with a composite plate instrumented with a PZT sensor network and some added masses as damages. The whole system is validated to different temperatures.
Palacios-Quiñonero, F.; Rubió-Massegú, J.; Rossell, Josep M.; Rodellar, J. ECCOMAS Thematic Conference Smart Structures and Materials p. 709-720 Data de presentació: 2017-06-06 Presentació treball a congrés
In this paper, the design and performance of partial-state controllers with incomplete
multi-actuation systems for the seismic protection of tall buildings is investigated. The proposed
approach considers a partially instrumented multi-story building with an incomplete system of
interstory force-actuation devices implemented at selected levels of the building, and an associated
set of collocated sensors that measure the interstory drifts and velocities corresponding to
the instrumented stories. The main elements of the proposed controller design methodology are
presented by means of a twenty-story building with two different actuation schemes. For these
control configurations, partial-state controllers are designed following a static output-feedback
H8 controller design approach, and the corresponding frequency and time responses are investigated.
The obtained results clearly indicate that the proposed partial-state controllers
are effective in mitigating the building seismic response. They also show up that a suitable
distribution of the instrumented stories is a relevant factor in the control system design.
Morphologic differentiation between abnormal lymphoid cells circulating in peripheral blood (PB) is a difficult task. The goal of this work is to define new features from image analysis that allow quantifying cytological variables in view of a further automatic recognition. We analyzed a total of 12574 digital cell images from 325 patients. PB films were stained with May Grünwald-Giemsa and images were obtained in the CellaVision® DM96. A total of 12 different lymphoid cells were included (Fig.1): mature normal lymphocytes (1085), abnormal lymphoid cells of chronic lymphocytic leukemia (2468), B-prolymphocytic leukemia (292), hairy cell leukemia (611), splenic marginal zone lymphoma (819), mantle cell lymphoma (1438), follicular lymphoma (1119), T-prolymphocytic leukemia (332), T-cell large granular lymphocytic leukemia (894) and Sézary lymphoid cells (756); reactive (1199) and blast lymphoid cells (1561) were also included. We extracted 2676 features (27 geometric and 2649 color and texture) from six color spaces and three regions of interest (nucleus, cytoplasm and the whole cell) for each cell image. Afterwards, by applying information theoretic feature selection, the 20 most relevant and less redundant for the automatic differentiation between the lymphoid cell groups included were analyzed. Statistically significant differences were obtained in all 20 most important features among the median values corresponding to the 12 lymphoid cells groups (p<0.0001). The nucleus/cytoplasm ratio was the best feature to distinguish the different lymphoid cells included in this work. Two additional geometric features were within the first 20: the external profile region (ranked 17th), also called hairiness, and the nuclear circularity (19th). The remaining 15 features that showed relevance for the discrimination among the lymphoid cells subsets were color/texture features (13 statistical and two granulometric). Only three color spaces (CMYK, RGB and HSV) and six color components were involved within them. The contribution of this work is a set of 20 new cytological variables with the following properties: 1) have quantitative formulations, 2) allow qualitative morphological interpretations useful for morphological diagnosis and 3) be efficient to automatically discriminate among a significant number of different lymphoid cell groups through a computerized system.
This paper presents a procedure to transform a chaotic logistic map into a continuous-time delay chaotic system by using sampled-data representation of continuous-time models. Because of this, the chaotic behavior of the resultant scheme is easy to proofread. A numerical illustration is also realized by utilizing Matlab/Simulink, where the new resultant chaotic attractor is shown
Palacios-Quiñonero, F.; Rubió-Massegú, J.; Rossell, Josep M.; Karimi, H.R. International Conference on Structural Engineering, Mechanics and Computation p. 46-52 Data de presentació: 2016-09 Presentació treball a congrés
In this paper, we present a novel control design methodology for structural vibration control of large buildings. The main idea consists in decomposing the overall building system into decoupled single-story subsystems and modeling the subsystem interactions as external disturbances. Then, a complete set of local decentralized controllers can be efficiently computed using the existing LMI solvers. In the proposed approach, two different levels of decentralization can be distinguished: decentralized design and decentralized implementation, which are both of critical importance in large-scale control problems. From the design point of view, the local controllers are independently synthesized using only the low-dimensional subsystem models. The implementation phase is through the overall decentralized controller defined by the set of local controllers, which can drive the actuation devices using only local state-feedback information. To illustrate the proposed methodology, decentralized H8 controllers are designed for the seismic protection of a five-story building and a twenty-story building. A proper set of numerical simulations is carried out to demonstrate the effectiveness of the proposed decentralized controllers and the computation times are considered to assess the computational effectiveness of the decentralized design methodology.
Camacho, J.; Ruiz, M.; Villamizar, R.; Mujica, L.E.; Perez-Gamboa, O. International Conference on Fracture and Damage Mechanics p. 107-110 DOI: 10.4028/www.scientific.net/KEM.713.107 Data de presentació: 2016-09 Presentació treball a congrés
In wind turbines (WTs), some faults can induce saturation of the control signal, and these saturation nonlinearities might lead to instability. Therefore, a robust system against saturation can better deal with faults. In this work, an avoid saturation strategy is proposed for the torque control of WTs. The key idea is that the reference power and generator speed set-points are hysterically manipulated. Simulation results from a 5MW benchmark model show that the proposed strategy has a clear added value with respect to the baseline controller not only in healthy condition but also in presence of a realistic fault.
Sanmiquel, L.; Bascompta, M.; Rossell, Josep M.; Oliva, J.; Anticoi, H.; Guasch, E. World Congress on Mechanical, Chemical, and Material Engineering (MCM'16) p. 1 Data de presentació: 2016-08-22 Presentació treball a congrés
Workplace health and safety is an essential element to take into account by companies, the administration responsible for it and employees themselves. According to stats from the Spanish Ministry of Employment and Social Security, the mining sector shows an accident rate, per 100.000 employees, 4.8 times higher than in other economic sectors. In addition, this rate is even higher when it is compared to the mining sector from USA or Australia, where it reaches 7 and 6.5 times respectively .
This study is focused on variables and behavioural patterns from the Spanish mining sector, either open pit or underground mining. Over-exertion is also discriminated from other types of accidents, because it represents around 20% of all accidents and an important time off work cause, which could cover results from other types of accident.
Data used was obtained from the Spanish Ministry of Employment and Social Security database. In itinere accidents have not been taken into account in the study, using only accidents during working time with at least one day lost.
The main goal of the investigation is to extract the underlying information from the database by means of data mining techniques  using the WEKA software, with the idea to determine better hazard prevention policies and reduce accidents at work in the mining sector.
We give an exhaustive characterization of singular weak solutions for a class of ordinary differential equations, which appears in the context of hydrodynamics. We construct peaked and cusped waves, fronts with finite-time decay and compact solitary waves.
The history of the hydrocarbons business in Colombia dates back to the early twentieth century where mining and energy sector has been one of the principal pillars for the its development. Thus, the pipelines currently in service have over 30 years and most of them are buried and phenomena like metal losses, corrosion, mechanical stress, strike by excavation machinery and other type of damages are presented. Since it can generate social and environmental problems, monitoring tools and programs should be developed in order to prevent catastrophic situations. However, the maintaining of these structures is very expensive and it is normally developed by foreign companies. In order to overcome this situation, recently the native research institute “Research Institute of Corrosion - CIC (Corporación para la Investigación de la Corrosión)” developed an in-line inspection tool to be operated in Colombian pipelines (especially gas) to get valuable information of their current state along of thousand kilometres. The recorded data is of big size and its processing demand a high computational cost and adequate tool analysis to determine a certain pipeline damage condition. On other hand, the author from UPC and UIS have been bringing its expertise in processing and analysing this type of big data by using mainly Principal Component Analysis (PCA) as an effective tool to detect and locate different damages. In previous papers, multidimensional data matrix was used to locate possible damages along the pipeline, however most of activated points were considered false alarms since they corresponded to weld points. Thus, in this paper it is proposed no considering piecewise weld points (tube sections) and an extension of PCA named Multiway PCA (MPCA) is applied for each each one of the tube sections that form the pipeline. Therefore, if a tube section is found outside from overall indices found by using the MPCA model, an alarm activated in that section and a precise location can be obtained by analyzing only data from that specific tube section.
Online monitoring systems demand an adequate operation of sensor system used to acquire structural state measurements. If a damaged sensor record is incorporated in the diagnosis algorithm, it could be generate uncertainties and generate unsuitable alarms. Thus,
appropriate operation of sensor system is a critical requirement in order to obtain a high reliability for structural damage diagnosis algorithms. In this work a data-driven procedure is studied in order to mitigate the faulty sensor effect in a monitoring system. The studied
method takes advantage of piezo-diagnostics approach, where piezoelectric devices are attached to the surface of the monitored structure to produce guided waves. Thus, piezoelectric measurements are analyzed by applying principal component analysis and cross-correlation, in order to detect abnormal behaviors. In this sense, the squared prediction error Q and Hotelling squared statistical indices are used to observe a typical behaviour caused by sensor problems or structural damages. The methodology is validated on a lab carbon steel pipe section by using scenarios that include electric power failures,
disconnecting power cords as well as mass adding. As concluding remark, in this work was possible to separate structural damage and fault sensor states at different clusters.
Mujica, L.E.; Ruiz, M.; Acho, L.; Alferez, E.; Tutivén, C.; Vidal, Y.; Rodellar, J. European Workshop on Structural Health Monitoring p. 1-10 Data de presentació: 2016-07-06 Presentació treball a congrés
The future of wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. Cost of operation and maintenance of offshore wind turbines is among 15-35% of the total cost. From this, 80% comes from unplanned maintenance due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for fault detection of wind turbine actuators and sensors faults in variable-speed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are converted into two-dimensional matrices to obtain gray-scale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture features are used: statistical, wavelet, granulometric and Gabor features. Then, the most significant features are selected with the conditional mutual criterion. Finally, the fault detection is performed using an automatic classification tool. In particular, a 10-fold cross validation is used to obtain a more generalized model and evaluate the classification performance. In this way, the healthy and faulty conditions of the wind turbine can be detected. Coupled non-linear aero-hydro-servo-elastic simulations of a 5MW offshore type wind turbine are carried out for several fault scenarios. The results show a promising methodology able to detect the most common wind turbine faults.
Palacios-Quiñonero, F.; Rubió-Massegú, J.; Rossell, Josep M.; Karimi, H.R. International Conference on Motion and Vibration Control p. 1-12 DOI: 10.1088/1742-6596/744/1/012163 Data de presentació: 2016-07-06 Presentació treball a congrés
In vibration control of adjacent buildings under seismic excitations, a twofold objective has to be considered:(i) to mitigate the vibrational response of the individual structures and (ii) to provide a suitable protection against interbuilding impacts (pounding). An interesting strategy to deal with this complex control problem consists in considering an integrated control system, which combines interbuilding actuation devices with local control systems implemented in the individual buildings. In this paper, an effective computational strategy to design this kind of integrated control systems is presented. The proposed design methodology is based on a linear matrix inequality formulation, allows including active and passive actuation devices, and makes it possible to deal with important information constraints associated to the problem. The main ideas are illustrated by means of a two-building system equipped with three actuation devices: two interstory actuation devices implemented at the ground level of the buildings, plus an interbuilding actuation device installed at the top level of the lowest building. For this control setup, two different integrated controllers are designed. A proper set of numerical simulations is conducted to assess the performance of the proposed controllers with positive results.
In vibration control of adjacent buildings under seismic excitations, a twofold objective has to be considered: (i) to mitigate the vibrational response of the individual structures and (ii) to provide a suitable protection against interbuilding impacts (pounding). An interesting strategy to deal with this complex control problem consists in considering an integrated control system, which combines interbuilding actuation devices with local control systems implemented in the individual buildings. In this paper, an effective computational strategy to design this kind of integrated control systems is presented. The proposed design methodology is based on a linear matrix inequality formulation, allows including active and passive actuation devices, and makes it possible to deal with important information constraints associated to the problem. The main ideas are illustrated by means of a twobuilding systemequipped with three actuation devices: two interstory actuation devices implemented at the ground level of the buildings, plus an interbuilding actuation device installed at the top level of the lowest building. For this control setup, two different integrated controllers are designed. A proper set of numerical simulations is
conducted to assess the performance of the proposed controllers with positive results.
This work discusses a methodology used to implement a data-driven strategy for Structural Health Monitoring. First, the instrumentation of the equipment is detailed by describing the main components to be installed in the test structure in order to produce guide d waves. Specifically, an active piezo active system is used for this purpose , which consists of piezoelectric devices attached to the test structure surface and an ac quisition system. Then, the programming procedure to embed the damage detection algorithm is defined. In particular, the mathematical foundations and software requirements for impleme nting the preprocessing stage, baseline model building, and statistical index computation are specified. As a result, the Odroid-U3 computational core has the capability t o perform online damage assessment. Finally, some validation tests are presented through videos and short real time demonstration. Experimental data are recorded from two test specimens: i.) a lab carbon steel pipe loop built to emulate leak scenarios, and ii.) an aluminum plate, where mass adding is used to emulate reversible damages. The results reported i n this work show the high feasibility of the proposal methodology for obtaining an online embedded monitoring system with several advantages such as low cost, easy configuration, expandability and few computational resources
This work is concerned with active vibration mitigation in wind turbines (WT) but not through the use of specifically tailored devices. Instead, a general control scheme is designed for torque and pitch controllers based on a super-twisting algorithm, which uses additional feedback of the fore-aft and side-to-side acceleration signals at the top of the WT tower to mitigate the vibrational behavior. In general, proposed methods to improve damping through pitch and torque control suffer from increased blade pitch actuator usage. However, in this work the blade pitch angle is smoothed leading to a decrease of the pitch actuator effort, among other benefits evidenced through numerical experiments. The most frequent faults induce vibrations in the corresponding WT subsystems. In fact, vibration monitoring has been recently used for fault diagnosis Thus, by means of vibration mitigation, different faulty conditions can be alleviated leading to a passive fault tolerant control. In this work, coupled non-linear aero-hydro- servo-elastic simulations of a floating offshore wind turbine are carried out for one of the most common pitch actuator faults.
Growing interest for improving the reliability of safety-critical structures, such as wind turbines, has led to the advancement of structural health monitoring (SHM). Existing techniques for fault detection can be broadly classified into two major categories: model-based methods and signal processing-based methods. This work focuses in the signal-processing-based fault detection by using principal component analysis (PCA) as a way to condense and extract information from the collected signals. In particular, the goal of this work is to select a reduced number of sensors to be used. From a practical point of view, a reduced number of sensors installed in the structure leads to a reduced cost of installation and maintenance. Besides, from a computational point of view, less sensors implies lower computing time, thus the detection time is shortened.
The overall strategy is to firstly create a PCA model measuring a healthy wind turbine. Secondly, with the model, and for each fault scenario and each possible subset of sensors, it measures the Euclidean distance between the arithmetic mean of the projections into the PCA model that come from the healthy wind turbine and the mean of the projections that come from the faulty one. Finally, it finds the subset of sensors that separate the most the data coming from the healthy wind turbine and the data coming from the faulty one.
Numerical simulations using a sophisticated wind turbine model (a modern 5MW turbine implemented in the FAST software) show the performance of the proposed method under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics.
Early damage detection remains one of the priorities of the structural health monitoring systems in the task of continuous monitoring. In this kind of systems different approaches can be used, however data-driven systems are requested because the information from the sensors is obtained directly from the structure in real operational and environmental conditions. Some of these approaches makes use of acousto-ultrasonics (AU) techniques, which offer the possibility of inspecting large areas of structures, by using a piezoelectric active sensor network. However, these kind of inspection systems are affected by the variations in the environmental conditions. In this sense, is a need to still working in more a nd better da ma ge detection techniques. This pa per descr ibes a hea lth monitor ing methodology combining the advantages of guided ultrasonic waves together with artificial immune systems as a pattern recognition technique to determine the effects of the temperature in the damage detection process, in addition, a sensor data fusion with the data from different temperatures is proposed as a hefty baseline to consider the healthy structure under different temperature conditions and discarding the resultant false positives by the changes in temperature. Experimental results are included to demonstrate the temperature effects and how the methodology improves the damage detection capabilities.
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: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the wind turbine is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data—the baseline and the data from the current wind turbine— are compared, a multivariate statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault- detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides an early fault detection, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.
Structural Health Monitoring is a growing area of interest given the benefits obtained from its use. This area includes different tasks in the damage identification process, among them, the most important is the damage detection at an early stage which enables to increase the security in mechanisms and systems, reducing risks and avoiding accidents. As a contribution in this topic, this work presents a data-driven methodology for the detection and classification of damages by using multivariate data driven approaches and machine learning algorithms which are validated and compared by using data from real structures in order to determine its behavior. In the methodology, PCA (Principal component analysis) and some pre-processing steps are used as the mechanisms to reduce data and build the features vector with relevant information about the different states of the structures under test. This methodology is validated by using some aluminum plates which are instrumented and inspected by means of PZT transducers attached to them and working in in several actuation phases. Results show a properly damage detection and classification of different simulated and real-damages.
Palacios-Quiñonero, F.; Rubió-Massegú, J.; Rossell, Josep M.; Karimi, H.R. European Congress on Computational Methods in Applied Sciences and Engineering p. 1-13 Data de presentació: 2016-06-06 Presentació treball a congrés
Distributed control systems for vibration control of large structures involve a large number of actuation devices and sensors that work coordinately to produce the desired control actions. Design strategies based on linear matrix inequality (LMI) formulations allow obtaining controllers for these complex control problems, which are characterized by large dimensionality, high computational cost and severe information constraints. In this paper, we conduct a comparative study of the computational effectiveness of three different LMI-based controller design strategies: H-infinity, energy-to-peak and energy-to-componentwise-peak. The H-infinity approach is a well-known design methodology and has been widely used in the literature. The
energy-to-peak approach is a particular case of generalized H2 design that is gaining a growing relevance in structural vibration control. Finally, the energy-to-componentwise-peak approach is a less common case of generalized H2 design that produces promising results among the three considered approaches. These controller design strategies are applied to synthesize active state-feedback controllers for the seismic protection of a five-story building and a twenty-story building both equipped with complete systems of interstory actuation devices. To evaluate the computational effectiveness of the proposed LMI design methodologies, the corresponding
computation times are compared and a suitable set of numerical simulations is carried out to assess the performance of the obtained controllers. As positive results, two main facts can be highlighted: the computational effectiveness of the energy-to-peak control design strategy
and the particularly well-balanced behavior exhibited by the energy-to-componentwise-peak controllers. On the negative side, it has to be mentioned the computational inefficiency of the considered LMI design methodologies to properly deal with very-large-scale control problems.
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.
The main objective of this paper is to present an academic example of a PD
controller applied to teach position control design of a DC-motor to automatically adjust a
potentiometer. This adjustment is focused on to solve the maximum power transfer objective
in a linear electrical circuit. This design involves the use of the extremum seeking algorithm. To
support our proposal, numerical simulations and mathematical modelling of the main problem
statement are programmed.
The continuous increase in the size of wind turbines (WTs) has led to new challenges in the design of novel torque and pitch controllers. Today’s WT control design must fulfill numerous specifications to assure effective electrical energy production and to hold the tower vibrations inside acceptable levels of operation. Hence, this paper presents modern torque and pitch control developments based on the super-twisting algorithm (STA) by using feedback of the fore- aft and side-to-side acceleration signals of the WT tower. According to numerical experiments realized using FAST, these controllers mitigate vibrations in the tower without affecting the quality of electrical power production. Moreover, the proposed controllers’ performance is better than the baseline controllers used for comparison.
The main contribution of this paper is to propose new control techniques which not only provide fault tolerance capabilities to the WT system, but also improve the overall performance of the system in both fault free and faulty conditions. Coupled non-linear aero-hydro-servo-elastic simulations of an offshore wind turbine with jacket platform are carried out. The proposed controllers are based in the super-twisting algorithm (STA) by using feedback of the generator shaft speed as well as the fore-aft and side-to-side acceleration signals of the WT tower.
Every physical actuator is subject to saturation. It has been well recognized that, when the actuator saturates, the performance of the closed-loop system (designed without considering actuator saturation) may seriously deteriorate. In extreme cases, the system stability may even be lost. This paper proposes an avoid saturation strategy for the torque controller of a wind turbine benchmark model. The simulation results show that the proposed strategy has a clear added value with respect to the baseline controller (well- accepted industrial controller) in the presence of faults. Another advantage of the contributed method is that conservative bounds for the actuator torque can be fixed in order to extend the service life of the wind turbine.