Matrix Differential Calculus with Applications in Statistics and Econometrics

https://ir.cwi.nl/pub/30568/1-s2.0-S096014811831231X-main.pdf

Machine learning methods for wind turbine condition monitoring: A review

A short overview of composite materials for wind turbine applications is presented here. Requirements toward the wind turbine materials, loads, as well as available materials are reviewed. Apart from the traditional composites for wind turbine blades (glass fibers/epoxy matrix composites), natural composites, hybrid and nanoengineered composites are discussed. Manufacturing technologies for wind turbine composites, as well their testing and modelling approaches are reviewed.

/pdf/materials-for-wind-turbine-blades-an-overview-3lipnnhhuf.pdf

Materials for Wind Turbine Blades: An Overview.

Structural health monitoring (SHM) techniques have been widely used in long-span bridges. However, due to limitations of computational ability and data analysis methods, the knowledge in ma...

Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection

Damage detection techniques for wind turbine blades: A review

Sensor distributions for structural monitoring: a correlation study

This study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to an SSP 34-m blade mounted on a test rig. Operational modal analysis was conducted to obtain mode shapes for undamaged and damaged states of the blade. Subsequently, the mode shapes were analyzed with one-dimensional continuous wavelet transformations for damage identification. The basic idea of the method is that structural damage will introduce local mode shape irregularities which are captured in the continuous wavelet transformation by significantly magnified transform coefficients, thus providing combined damage detection, localization, and size assessment. It was found that due to the nature of the proposed method, the value of the identification results highly depends on the number of employed measurement points. Since a limited number of measurement points were utilized in the experiments, only certain dama...

/pdf/operational-modal-analysis-and-wavelet-transformation-for-3nyx9fvqq9.pdf

Operational modal analysis and wavelet transformation for damage identification in wind turbine blades

https://hal.inria.fr/hal-01619442/document

Statistical methods for damage detection applied to civil structures

The present paper explores the application of a well-established vibration-based damage detection method to an operating Vestas V27 wind turbine blade. The blade is analyzed in a total of four states, namely, a healthy one plus three damaged ones in which trailing edge openings of increasing sizes are introduced. In each state, the blade is subjected to controlled actuator hits, yielding forced vibrations that are measured in a total of 12 accelerometers; of which 11 are used for damage detection. The dimensionality of these acceleration data is reduced by means of principal component analysis (PCA), and then a reduced set of selected principal scores are employed as damage features in the Mahalanobis metric in order to detect damage-induced anomalies.

Damage detection in an operating Vestas V27 wind turbine blade by use of outlier analysis

The paper presents a case study demonstrating the methods used to estimate the value of the information (VoI) delivered by a structural health monitoring (SHM) system. The monitored object is a bla...

Martin Dalgaard Ulriksen

Papers

Sensor distributions for structural monitoring: a correlation study

Operational modal analysis and wavelet transformation for damage identification in wind turbine blades

Statistical methods for damage detection applied to civil structures

Damage detection in an operating Vestas V27 wind turbine blade by use of outlier analysis

A case study on risk-based maintenance of wind turbine blades with structural health monitoring