Sequential projection pursuit for optimised vibration-based damage detection in an experimental wind turbine blade

Abstract

To advance the concept of smart structures in large systems, such as wind turbines, it is desirable to be able to detect structural damage early while using minimal instrumentation. Data-driven vibration-based damage detection methods can be competitive in that respect because global vibrational responses encompass the entire structure. Multivariate damage sensitive features (DSFs) extracted from acceleration responses enable to detect changes in a structure via statistical methods. However, even though such DSFs contain information about the structural state, they may not be optimized for the damage detection task. This paper addresses the shortcoming by exploring a DSF projection technique specialized for statistical structural damage detection. High dimensional initial DSFs are projected onto a low-dimensional space for improved damage detection performance and simultaneous computational burden reduction. The technique is based on sequential projection pursuit where the projection vectors are optimized one by one using an advanced evolutionary strategy. The approach is applied to laboratory experiments with a small-scale wind turbine blade under wind-like excitations. Autocorrelation function coefficients calculated from acceleration signals are employed as DSFs. The optimal numbers of projection vectors are identified with the help of a fast forward selection procedure. To benchmark the proposed method, selections of original DSFs as well as principal component analysis scores from these features are additionally investigated. The optimized DSFs are tested for damage detection on previously unseen data from the healthy state and a wide range of damage scenarios. It is demonstrated that using selected subsets of the initial and transformed DSFs improves damage detectability compared to the full set of features. Furthermore, superior results can be achieved by projecting autocorrelation coefficients onto just a single optimized projection vector.

Surface Damage Identification of Wind Turbine Blade Based on Improved Lightweight Asymmetric Convolutional Neural Network

“…Dolin'ski et al [7] used the Finite Element Method (FEM) and Laser Scanning Vibrometry (LSV) to determine the size and location of stratification in WTBs. Hoell et al [8] proposed a data-driven vibration-based damage detection method, which uses multivariate Damage Sensitive Features (DSFs) extracted from acceleration responses to conduct damage detection. The experimental results showed that the best damage detection accuracy of 79.2% was obtained using sequential projection pursuit (SPP)-converted DSFs.…”

Anomaly detection for large span bridges during operational phase using structural health monitoring data

“…In general, the vibration-based methods are divided into two categories, namely, traditiontype and modern-type [14]. The tradition-type is mainly based on the structural vibration characteristics such as variation of natural frequencies or mode shapes [15][16][17][18]. The moderntype makes use of signal-processing techniques or artificial intelligence, including wavelet-based approaches, neural networks, etc [19][20][21][22][23].…”