Welcome to AVANT-SHM by

Luis David Avendaño-Valencia

Dive into topics related to Structural Health Monitoring on civil and mechanical structures: Modal analysis, damage diagnosis, fatigue life assessment.

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Publications

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Citations

Awards

Research Topics

In this stage of my career, I have established the following research directions, and my main contributions up to date pertain to the following topics, where I have managed to construct a data-driven framework for SHM under Environmental and Operational Variability (EOV).

Long-term analysis of the vibration response of structures

When observed over long analysis intervals, structures will display large variations in their dynamic properties due to the constantly evolving environmental and operational conditions. This may have dramatic effects on their lifetime performance (fatigue accumulation) and damage diagnosis. Back from my PhD years, I’ve been working on different topics associated with this type of slowly-evolving non-stationary behavior. Some of these topics are discussed further below.

Anomaly detection and useful lifetime assessment based on operational data

Complex structures are often instrumented with several sensors monitoring hundreds of variables related to the operation of structural components and systems. The obtained data is characterized by large volumes, but also is heterogeneous and complex. However, important information on the structural condition and remaining lifetime of components could be extracted with the application of proper data analysis algorithms. The selected topics below are examples of such methods, where we use different machine learning algorithms to make sense of the operational data acquired in wind turbines and marine engines. performance (fatigue accumulation) and damage diagnosis. Back from my PhD years, I’ve been working on different topics associated with this type of slowly-evolving non-stationary behavior. Some of these topics are discussed further below.

Machine Learning for Condition Monitoring of Wind Turbine Drivetrains

Vibration-based Condition Monitoring (CM) is based on the analysis of the vibration responses measured on rotating machinery. Such vibration responses are quite complex, and as a result, modern CM algorithms make use of advanced machine learning methods based on deep learning to obtain reliable damage assessment. The construction and training of ML architectures is not straightforward, particularly for wind turbine drivetrains, where the highly variable operational conditions and the lack of information on damages are main challenges. The topics below correspond to recent project ideas, where we attempt to overcome these hurdles.

Teaching Experience

Through my career I have had the opportunity to teach at both undergraduate and graduate levels.

I enjoy the teaching activity, and I assume it as the labor of communicating my own experience and reaffirming my own knowledge of the specific topics. Likewise, I have the feeling of connection with my students, and as a result, the success of each student is my primary goal.

Featured Publications

“Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression”

L.D. Avendaño-Valencia, I. Abdallah and E.N. Chatzi, “Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression”, Renewable Energy, 170, pp. 539-561, June 2021.

“On explicit and implicit procedures to mitigate environmental and operational variabilities in data-driven structural health monitoring”

D. García-Cava, L.D. Avendaño-Valencia, A. Movsessian, C. Roberts, and D. Tcherniak, “On explicit and implicit procedures to mitigate environmental and operational variabilities in data-driven structural health monitoring”, in eds. A. Cury, D. Ribeiro, F. Ubertini, and M.D. Todd, Structural Health Monitoring Based on Data Science Techniques, special collection on Structural Integrity, Springer, 2021.

“Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines”

L.D. Avendaño-Valencia, E.N. Chatzi and D. Tcherniak, “Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines”, Mechanical Systems and Signal Processing, 142, 106686, August 2020.

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Main areas of interest:

Structural dynamics and operational modal analysis in time-dependent and non-linear conditions
Structural Health Monitoring and remaining useful lifetime prognosis
Dynamics of structures under changing environment
Non-stationary, cyclo-stationary and non-linear statistical signal processing
Surrogate modelling and digital twin technologies