L.D. Avendano-Valencia

University of Southern Denmark | SDU · Department of Mechanical and Electrical Engineering

Constructed in 1969, the Chillon viaducts constitute a vital component of the Swiss roadway network. The bridge deck was strengthened in 2015 by means of an Ultra High Performance Fiber Reinforced Cement-based Composite for alleviating the effects of deterioration that has been induced by Alkali-Silica Reaction. In order to assess the efficacy of t...

Gaussian Process (GP) time-series models are a special type of models for Linear Parameter Varying (LPV) systems in which the parameters are represented as stochastic variables following a Gaussian Process regression of the scheduling variables. GP time-series models are ideal for the representation of LPV systems where some of the scheduling varia...

Aim: (1) To quantify the invisible variations of facial erythema that occur as the blood flows in and out of the face of diabetic patients, during the blood pulse wave using an innovative image processing method, on videos recorded with a conventional digital camera and (2) to determine whether this "unveiled" facial red coloration and its periodi...

Changing operational conditions pose an important challenge for efficient implementation of structural health monitoring practices (SHM). Structural properties are influenced by changes in Environmental and Operational Parameters (EOPs), often leading to a non-stationary response. This issue has been tackled by the authors through introduction of a...

The expected growth of new offshore wind turbine installations necessitates effective Operation and Maintenance strategies to ensure wind farm reliability. Key to these strategies is optimizing operational uptime for Crew Transfer Vessels (CTVs), which transport technicians to wind farms. However, CTV operations are often cancelled due to harsh wea...

Design, monitoring and control of complex structures require methods to accurately model the system dynamics. In this regard, parametric system identification techniques operating on experimental or operational data can potentially provide computationally efficient, compact, and accurate representations of the dynamics of complex systems. The succe...

Finite Element Model Updating (FEMU) plays a pivotal role in enhancing the accuracy and reliability of structural dynamics models. Nonetheless, uncertainties related to experimental vibration testing and natural manufacturing variability introduce unavoidable variability in the results of Modal Parameter Extraction (MPE), which is the basis for upd...

One of the major challenges in the development of vibration-based structural health monitoring is the influence of environmental and operational parameters (EOPs) on damage-sensitive features (DSFs). With multiple methods already available in the literature, a challenge remains on the compensation of partially measurable and noisy environmental and...

The anticipated expansion of offshore wind turbine installations requires robust Operation and Maintenance (O&M) strategies to ensure the reliability of wind farms. Central to these strategies is maximizing the operational uptime of Crew Transfer Vessels (CTVs), which transport technicians to the wind farms. CTVs operate in harsh environments and p...

The challenge that is at the forefront of data-driven vibration-based structural health monitoring (VSHM) is the detrimental effect caused by environmental and operational variations (EOVs). Therefore, action must be taken in order to mitigate the effects of the EOVs without affecting the influence of damage. A number of regression-based approaches...

Today, the green transition is being pursued more than ever, leading to a significant increase in the installation of wind turbines and wind farms. Maintaining and servicing the wind turbines is accompanied by extensive expenses. Therefore, predictive maintenance techniques are in increasing demand, including the implementation of a well-functionin...

Axial piston pumps (APP) are energy efficient while operating, but they are prone to catastrophic failures by virtue of their construction and tight tolerances. Reliability can be improved either by (i) increasing the robustness of each component used in the APPs or (ii) predicting their health status by using different techniques, so that failures...

Current damage diagnosis algorithms robust to environmental and operational variations require training datasets containing sufficient information from the operational domain of the structure. In most cases, this translates in extended monitoring periods, which delays the applicability of SHM methodologies. This work concerns to the development of...

Axial piston pumps (APP) are energy efficient while operating, but they are prone to catastrophic failures by virtue of their construction and tight tolerances. Reliability can be improved either by (i) increasing the robustness of each component used in the APPs or (ii) predicting their health status by using different techniques, so that failures...

Within the context of vibration-based condition monitoring, virtual sensing techniques facilitate vibration sensing at locations where sensors are not set at the time of inspection. In this work we postulate a data-driven frequency-domain virtual sensing procedure, based on Cross Power Spectral Density (CSD) matrices obtained from an initial dense...

This chapter summarizes recent developments in data-centric monitoring of wind farms. We present methodologies which share information from multiple sources. Problems include inter-turbine modeling of wind speed and wake effects; methods for combining multiple emulators; spatially distributed virtual sensing, for the estimation of dynamic loads; hi...

Maintaining the condition of a vessel and its equipment guarantees the scheduled completion of voyages and the safety of the crew. This paper presents condition monitoring techniques for early detection of faults related to piston rings in remote cylinders of two-stroke marine diesel engines. Operational sensor data from the main engine of a contai...

Wind turbine drivetrain vibrations are characterised by very complex dynamics originating from the convolution of deterministic and stochastic excitation sources moving through the structural dynamics of the wind turbine. In this work we postulate Linear Parameter Varying Vector AutoRegressive (LPV-VAR) models to represent those signals. To deal wi...

Slides of the presentation in SMART ECCOMAS 2023. Paper can be found in: https://www.researchgate.net/publication/372139644_A_Fast_Identification_Algorithm_for_Linear_Parameter_Varying_Vector_AR_Models_of_Short-Term_Drivetrain_Vibration

Today, the green transition is being pursued more than ever, leading to a significant increase in the installation of wind turbines and wind farms. Maintaining and servicing the wind turbines is accompanied by extensive expenses. Therefore, predictive maintenance techniques are in increasing demand, including the implementation of a well-functionin...

Mitigation of Environmental and Operational Variabilities (EOVs) remains one of the main challenges to adopt Structural Health Monitoring (SHM) technologies. Its implementation in wind turbines is one of the most challenging due to the adverse weather and operating conditions these structures have to face. This work proposes an EOV mitigation proce...

This work is concerned with the harmonic decomposition of pseudo-periodic non-stationary multivariate signals. In this framework, a signal component corresponds to a single sinusoid with time-dependent amplitude and frequency. Thus, in the proposed harmonic decomposition, all the signal components are bound to share the same fundamental time-depend...

Mitigation of Environmental and Operational Variabilities (EOVs) remains one of the main challenges to adopt Structural Health Monitoring (SHM) technologies. Its implementation in wind turbines is one of the most challenging due to the adverse weather and operating conditions these structures have to face. This work proposes an EOV mitigation proce...

The work of this conference paper considers a simplified cartesian manipulator of a gantry robot set up as a pinned-pinned Euler-Bernoulli beam with the purpose of analysing the structural dynamics when subjected to a moving load/cart and a surface roughness. A physics-based beam-cart model is created utilising principles of physical domain substru...

In the long-term, the modal properties of Offshore Wind Turbines (OWT) exhibit significant variations due to constant changes in environmental and operational conditions. These variations have a substantial effect on the wind turbine loads and will lead, for instance, to large uncertainty in fatigue and useful life predictions. Due to the numerous...

A significant issue that has plagued data-driven Vibration-based Structural Health Monitoring (VSHM) is the mitigation of Environmental and Operational Variations (EOVs). The Damage Sensitive Features (DSFs) that are obtained from the vibration response of the structure are influenced by EOVs. Regression analysis, such as multivariate nonlinear reg...

Current damage diagnosis algorithms robust to environmental and operational variations require training datasets containing sufficient information from the operational domain of the structure. In most cases, this translates in extended monitoring periods, which delays the applicability of SHM methodologies. This work concerns to the development of...

Structural damping is a critical quantity for condition assessment and fatigue lifetime prediction in wind energy. Its estimation from operational vibration data comes with a considerable amount of uncertainty, derived from environmental and operational variability and from the quality of the estimation process itself. In this paper, we aim to dete...

Assessment of damping contributions is a major concern in the design and fatigue life analysis of Offshore Wind Turbines (OWTs), which include contributions from structural, aeroelastic, hydroelastic, and soil dynamics, and oftentimes from an additional tuned mass-damper. Thus, the vibration damping value de-pends on the current Environmental and O...

From the early introduction of ML in the context of SHM, performance assessment of damage detection and classification algorithms is routinely made in terms of traditional ML performance figures. In parallel, most benchmark problems consider a series of discrete states (healthy vs. damaged, or healthy and various target damages), and damage diagnos...

When designing regression models for robust EOV mitigation, it is not easy to design the models so that they accurately represent the varying conditions. In this work, a nonlinear stepwise method is implemented to choose the most influential parameters so that no unnecessary uncertainty is added to the models. A problem associated with large featur...

A significant problem associated with the implementation of Vibration-Based Structural Health Monitoring (VSHM) systems originates from the detrimental effects caused by Environmental and Operational Variations (EOVs). The EOVs cause observations from the same structural condition to behave in different manners. As such, this leads to issues when d...

Electrocardiographic (ECG) signals comprise pseudo-periodic non-stationary signals, which are also bound to significant interferences. Their pseudo-periodic structure could make them amenable to harmonic decompositions based on oscillatory components with time-dependent amplitude and frequency. Current methods, like the Empirical Mode Decomposition...

Vibration-based Structural Health Monitoring (VSHM) is becoming one of the most commonly used methods for damage diagnosis and long term monitoring. In data-driven VSHM methods, Damage Sensitive Features (DSFs) extracted from vibration responses are compared with reference models of the healthy state for long-term monitoring and damage identificati...

Structures experience through their operational lifetime significant variations in their vibrational response characteristics. These variations stem from natural deterioration and damage accrual, effectively degrading their operational performance slowly and steadily in the long term. Concurrently, Environmental and Operational Variability (EOV) in...

This work introduces a parametric modal decomposition method for multivariate non-stationary signals based on a block-diagonal time-dependent state space representation and Kalman filtering/smoothing. Each second-order block is constructed with the real and imaginary parts of each mode instantaneous eigenvalues, and thus represents a single non-sta...

This is the presentation for our paper "State-space modal representations for decomposition of multivariate non-stationary signals" presented in the 19th IFAC Symposium on System Identification (Conference paper in https://www.researchgate.net/publication/353345403_State-space_modal_representations_for_decomposition_of_multivariate_non-stationary_s...

We propose data-driven models to predict the loads acting on different components of a wind turbine. These models use SCADA, wind inflow and other variables to predict loads in components of interest of a wind turbine. In this work, we validate this approach on actual wind turbine data from the Alpha Ventus Wind Farm obtained within the framework o...

Among the principal challenges in the analysis of vibration monitoring data relates to capturing and understanding the significant variability in the underlying dynamics. In fact, over long periods, the dynamics of any structural system are time-dependent as a result of Environmental and Operational Variability (EOV), which includes changes in the...

Diabetes is currently one of the major public health threats. The essential components for effective treatment of diabetes include early diagnosis and regular monitoring. However, health-care providers are often short of human resources to closely monitor populations at risk. In this work, a video-based eye-tracking method is proposed as a low-cost...

We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input...

For data-driven vibration-based structural health monitoring (VSHM) systems to be considered reliable they must overcome the challenge of mitigating the environmental and operational variability (EOV) on the vibration features. This is particularly important in large and exposed structures such as wind turbine blades (WTB). This work aims to unders...

This work introduces a parametric modal decomposition method for multivariate non-stationary signals based on a block-diagonal time-dependent state space representation and Kalman filtering/smoothing. Each second-order block is constructed with the real and imaginary parts of each mode instantaneous eigenvalues, and thus represents a single non-sta...

In vibration-based structural health monitoring (SHM), environmental and operational variabilities (EOVs) can mask damage-induced changes in the vibration response and thus pose limitations to the damage detection sensitivity. Numerous approaches applying supervised as well as unsupervised learning methods have been suggested to reduce the distorti...

Presentation for the EURODYN 2020 Conference - System Identification Minisymposium

We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input...

Critical to railway infrastructure assessment is the tracing of interaction between railway vehicle and track.This is a non-trivial task characterized by non-stationary dynamics appearing due to changing operational conditions. To ensure reliable dynamic characterization of rail infrastructure, we propose a modeling methodology of locally non-stati...

The analysis presented in this work relates to the quantification of the effect of a selected set of measured Environmental and Operational Parameters (EOPs) on the dynamic properties of low and high frequency vibration, in the context of a vibration monitoring campaign implemented on the blade of an operating wind turbine. To this end, a Gaussian...

This data set contains the flapwise vibration response simulation of a wind turbine blade under Environmental and Operational Variability (EOV) as well as increasing damage. The blade’s dynamics are represented by means of a 4 element FEM of a cantilever beam, while dynamic loading corresponds to a discretized turbulent wind field calculated with t...

While the concept of structural monitoring has been around for a number of decades, it remains under-exploited in practice. A main driver for this shortcoming lies in the difficulty to robustly and autonomously interpret the information that is extracted from dynamic data. This hindrance in properly deciphering the collected information may be attr...

Gaussian Process (GP) time-series models are a special type of models for Linear Parameter Varying (LPV) systems in which the parameters are represented as stochastic variables following a Gaussian Process regression of the scheduling variables. GP time-series models are ideal for the representation of LPV systems where some of the scheduling varia...

Discusses the application of GP time-series models on structural health monitoring data, considering two main challenges: 1) slowly varying time-dependent dynamics due to environmental and operational variability; 2) large monitoring data volumes. Presented in the 3rd IFAC Workshop on Linear Parameter Varying Systems

Located at the shore of Geneva Lake, in Switzerland, the Chillon viaducts are two parallel structures consisted of post-tensioned concrete box girders, with a total length of 2 kilometers and 100m spans. Built in 1969, the bridges currently accommodate a traffic load of 50.000 vehicles per day, thereby holding a key role both in terms of historic v...

Ice throw from the blades of operational wind turbines is a safety concern when wind turbines are installed in a densely built environment. While several commercial solutions exist to detect icing or prevent ice build up altogether, there is still a desire for a more effective low-cost solution. In this contribution a previously instrumented onshor...

This work is devoted to the problem of the decomposition of a non-stationary signal into modal components, for which a methodological approach based on diagonal time dependent state space models is postulated. In particular, on this paper is shown that the response of a diagonal time-dependent state space models can be cast into a modal form charac...

Wind turbines structures are described by complex dynamics operating under a wide range of environmental and operational conditions. Amongst these, the varying nature of the wind excitation forms perhaps the main driver for the variability of the induced dynamics. In this sense, the features of the dynamic response of an up-wind wind turbine are ex...

This presentation discusses the application of Gaussian Process Regression Time Series models in the identification, analysis and health monitoring of structures operating under changing operational conditions. We provide an overview of estimation methods as well as schemes for robust decision making, considering the problem of incompleteness of tr...

Recent advances in sensor technologies, with associated reduction in corresponding costs, have revealed the potential of vibration-based monitoring methods to escort structures throughout their life-cycle, and to support decisions for their optimal management. However, in deciphering the monitored information several shortcomings need to be overcom...

This work addresses the problem of vibration-based damage detection on structures operating under significant levels of uncertainty originating from variable environmental and operational conditions. For this purpose, a Gaussian Process Regression Vector AR (GPR-VAR) model is postulated for the representation of the vibration response of a structur...

A wide range of vibrating structures are characterized by variable structural dynamics resulting from changes in environmental and operational conditions, posing challenges in their identification and associated condition assessment. To tackle this issue, the present contribution introduces a stochastic modeling methodology via Gaussian Process (GP...

The problem of vibration–based damage diagnosis in structures characterized by time–dependent dynamics under significant environmental and/or operational uncertainty is considered. A stochastic framework consisting of a Gaussian Mixture Random Coefficient model of the uncertain time–dependent dynamics under each structural health state, proper esti...

Wind turbines comprise structures of highly complex dynamics, operating under significant variations in environmental and operational conditions. Effective monitoring and fatigue prediction algorithms for these structures require an accurate representation of their dynamic response on both the short-and long-term scale. The latter may be achieved i...

The study focuses on vibration response based health monitoring for an operating wind turbine, which features time-dependent dynamics under environmental and operational uncertainty. A Gaussian Mixture Model Random Coefficient (GMM–RC) model based Structural Health Monitoring framework postulated in a companion paper is adopted and assessed. The as...

This work deals with the identification of non-stationary/time-dependent dynamics based on vector measurements by means of postulated Linear Parameter Varying Vector AutoRegressive models, applied to the identification of the vibration response of an operating wind turbine blade. The focus here lies in estimation of the model parameters and their c...

Originally presented in the 1st CSZ Block Course - UQ and Data Analysis in Applied Sciences. Example codes and resources available under request.

This work deals with the identification of non-stationary/time-dependent dynamics based on vector measurements by means of postulated Linear Parameter Varying Vector AutoRegressive models, applied to the identification of the vibration response of an operating wind turbine blade. The focus here lies in estimation of the model parameters and their c...