Modal Analysis for Crack Detection in Small Wind Turbine Blades

ABSTRACTThe structural stiffness of a blade changes when it is defective, which affects its modal parameters. Consequently, modal parameters can be crucial to investigating defects. Therefore, developing a model that can accurately analyze the modal parameters of healthy and defective blades is essential. This study proposes a high‐fidelity finite element (FE) model development method based on multiobjective optimization. Three 10‐kW‐class wind turbine blades were manufactured, and the modal parameter changes due to debonding damage were measured. The initial FE model was constructed using structural drawings and considering specific material properties. Subsequently, the modal analysis results were compared with the experimental results. The analysis errors of normal and abnormal blades were expressed as cost functions and multiobjective optimization was performed. Machine learning–based predictive models facilitated time‐ and cost‐effective optimization, enabling modal updating that simultaneously reflects modal parameter changes for various blade models. Because accurately predicting the dynamic characteristics of blades by various defect locations and sizes is crucial for blade damage detection, the proposed method is significant for damage detection research.

This study investigates the feasibility of an alternative structural health monitoring (SHM) technique for an existing, post-tensioned, prestressed concrete bridge in Niigata, Japan. Currently, a static SHM system is in place to detect the progress of damages within the bridge. However, the existing system cannot properly monitor the structural health of the bridge including the periodic vibration, which is one of the damage-sensitive features of interest. Therefore, to effectively detect the real-time performances of the bridge, a three-dimensional (3D) Finite Element (FE) model was developed as a reference and verified using on-site load-deflection test results. After validating the reference FE model, different damage scenarios, such as degradation of concrete, corrosion & rupture of steel tendons and missing tendons, were incorporated in the FE models. Based on non-linear structural and Eigenvalue analyses, natural frequencies and mode shapes of the bridge remain constant even after careful consideration of all types of damages in the FE model. However, vertical displacements are observed to increase for the damage scenarios. Although the effect of tendon rupture and corrosion showed negligible influences on the vertical displacement, the deterioration of the concrete largely influenced the vertical displacement. Additionally, crack widths were found to vary with damage types. Brifely, this study recommends some effective indicators to monitor the structural conditions of the bridge using FE analysis (FEA).

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.

One of the most critical components of wind turbines is the blades. As blades are constantly exposed to weather and high loads, they can wear and become damaged. Performing in-situ continuous monitoring can help detect sudden damages and prevent further degradation, which is imperative to avoid economic losses and fatal accidents. However, monitoring a rotating rotor blade presents some challenges. One challenge is the consideration of lightning protection. Hence, a non-conductor measurement system must be used. Another challenge is system identification and modal tracking considering environmental and operational conditions (EOCs). Furthermore, vibration-based monitoring results from actual and operational rotor blades are scarce in the existing literature. This study centres on operational modal analysis (OMA) using covariance- driven stochastic subspace identification (SSI-COV) of an offshore wind turbine blade in operation employing eight months of data from fibre optic accelerometers (FOA) and fibre Bragg grating (FBG) strain gauges. The identification focuses on the first two bending modes in flapwise and edgewise directions and the first torsional mode. The study aims to determine which fibre optic sensor delivers better results. Furthermore, it addresses the effect of EOCs on the modal parameters, where system identification and modal tracking are discussed. It was found that tracked modes showed similar trends with respect to time, and a high connection was observed between wind speed, rotor speed, and pitch angle with frequency and damping. Additionally, acceleration and strain measurements deliver the same frequency and damping values. FBG better identified the first flapwise mode. On the other hand, FOA was superior in almost all other modes. This study points towards employing FOA for vibration-based monitoring of wind turbine blades using modal parameters.

This paper reports the structural health monitoring benchmark study results for the Canton Tower using Bayesian methods. In this study, output-only modal identification and finite element model updating are considered using a given set of structural acceleration measurements and the corresponding ambient conditions of 24 hours. In the first stage, the Bayesian spectral density approach is used for output-only modal identification with the acceleration time histories as the excitation to the tower is unknown. The modal parameters and the associated uncertainty can be estimated through Bayesian inference. Uncertainty quantification is important for determination of statistically significant change of the modal parameters and for weighting assignment in the subsequent stage of model updating. In the second stage, a Bayesian model updating approach is utilized to update the finite element model of the tower. The uncertain stiffness parameters can be obtained by minimizing an objective function that is a weighted sum of the square of the differences (residuals) between the identified modal parameters and the corresponding values of the model. The weightings distinguish the contribution of different residuals with different uncertain levels. They are obtained using the Bayesian spectral density approach in the first stage. Again, uncertainty of the stiffness parameters can be quantified with Bayesian inference. Finally, this Bayesian framework is applied to the 24-hour field measurements to investigate the variation of the modal and stiffness parameters under changing ambient conditions. Results show that the Bayesian framework successfully achieves the goal of the first task of this benchmark study.

Performance comparison of Kalman−based filters for nonlinear structural finite element model updating

A review of full-scale structural testing of wind turbine blades

Experimental and FE investigations on the influential parameters in positioning and measurement of strain gauges in adhesively bonded single lap joints

Vibration-based approach for structural health monitoring of ultra-high-performance concrete bridge

A reliable and accurate analytical model is desired for the printed circuit board (PCB) with IC package to predict the system response due to loadings such as shock and vibration simulation or even with thermal effect. This work addresses the procedure of model verification by the adoption of experimental modal analysis (EMA) to validate the finite element (FE) model constructed by FE commercial software. The PCB with one package adhered with the heating pad to emulate the heat effect is first considered for completely free boundary condition. The refined FE model of the PCB consists of detail components, such as the chip, substrate, compound and solder balls. The thermal effect on the PCB is simulated to conduct the temperature field analysis as well as the thermal stress. The modal analysis on the PCB with the heating in steady state is then performed to obtain the structural modal parameters, i.e. natural frequencies and mode shapes. The EMA is also carried out to determine the system modal properties that are used to update the analytical FE model. Through the comparison of frequency response functions and modal parameters between the analytical FE model and the real PCB structure, the refined FE model can be verified for material properties and thermal boundary conditions. The same procedure for model verification is then conducted via both EMA and FEA on the PCB in the fixed boundary that complies with the test fixture for the random vibration test of JEDEC specification. The verified equivalent FE model of the PCB can then be adopted to perform spectrum response analysis accordingly.

The rapid deployment of satellites is hindered by the need to flight-qualify their components and the resulting mechanical assembly. Conventional methods for qualification testing of satellite components are costly and time consuming. Furthermore, full-scale vehicles must be subjected to launch loads during testing. The focus of this research effort was to assess the performance of Structural Health Monitoring (SHM) techniques to replace the high-cost qualification procedure and to localize faults introduced by improper assembly. SHM techniques were applied on a small-scale structure representative of a responsive satellite. The test structure consisted of an extruded aluminum spaceframe covered with aluminum shear plates, which was assembled using bolted joints. Multiple piezoelectric patches were bonded to the test structure and acted as combined actuators and sensors. Piezoelectric Active-sensing based wave propagation and frequency response function techniques were used in conjunction with finite element modeling to capture the dynamic properties of the test structure. Areas improperly assembled were identified and localized. This effort primarily focused on determining whether or not bolted joints on the structure were properly tightened.

In this paper, we discuss the dynamics of beams with multiple delaminations. The objective of the study is to use the dynamic response of the delaminated beams in health monitoring of composite structures. As a first step, we present experimental results on the vibrations of a cantilever beam with multiple delaminations. These experiments are conducted by exciting the beam with piezoelectric actuators. The response is detected by using a high-speed video camera to study the existence of delamination opening modes in a beam with multiple delaminations. Then, an analysis of natural frequencies and mode shapes, for a delaminated beam, is presented by using analytical techniques. The measured dynamic responses of the beam with multiple delaminations are also presented. The experimentally detected nonlinear response is discussed. Introduction Delaminations or interlayer cracks and impact damages are typical defects observed in composite structures. The presence of these defects changes the dynamic characteristics of the structure . Some of the specific changes in the observed dynamic characteristics include changes in natural frequencies, changes in the mode shapes and changes in damping ratios. These changes can be used to identify the existence, location and magnitude of a delamination or an impact damage, before they can grow to their critical sizes. Studies, to date, are concerned with the dynamics of beams and plates with a single delamination. In this paper, we specifically discuss the structural dynamics of a beam with multiple delaminations. This information can be used to develop health-monitoring techniques for composite beams with multiple delaminations. In an earlier paper, Luo and Hanagud studied the effects of defects, like saw-cuts, impact damage and delamination, on the structural dynamic characteristics of composite beams. They have shown that perturbations in natural frequencies and modes of a beam, due to defects, can be used for health monitoring of structures. In particular, it has been shown that an integral equation that considers perturbations in curvature modes and natural frequencies can be very effectively used to detect local defects. To distinguish delaminations from other types of monitored (identified) defects Luo and Hanagud have discussed nonlinear effects like superharmonic response in the dynamic response of a harmonically excited delaminated beam. They considered only a single delamination. In this paper we study beams with multiple delaminations. More specifically, we discuss dynamic response and the changes in natural frequencies, mode shapes due to multiple delaminations in a composite beam. One of the earliest models for vibration analysis of composite beams with delaminations was proposed by Ramkumar et al. This model simply used four Timoshenko beams connected at the delamination edges to model a composite beam with one throughwidth delamination. The predicted frequencies, based on this model, were consistently lower than the results of experimental measurements. Wang et al. improved the analytical solution by including coupling between flexural and axial vibrations of the delaminated sublaminates. Using an isotropic beam with splits and the classical beam model, they found that the calculated natural frequencies were closer to experimental results. With similar considerations, Nagesh Gummadi and Hanagud formulated a finite element solution for arbitrary composite beams. In the finite element models, they considered a classical composite beam model as well as the beam model with high order shear deformations. Mujumdar and Suryanarayan have proposed a model which imposed a constraint between the delaminated sublaminates to force them to have the same flexural displacement. This model was unable to * Ph.D. Student, School of Aerospace Engineering † Professor, School of Aerospace Engineering, AIAA Member Copyright © 1999 The American Institute of Aeronautics and Astronautics Inc. All rights reserved

Acoustic Emission (AE) technique is non-destructive Structural Health Monitoring (SHM) technique which has the potential to detect internal damages in real time. Literature reveals that researchers have used three-dimensional (3d) Finite Element Method (FEM) to predict AE waveforms in isotropic and single-layer anisotropic plates. However, the study to predict acoustic emission waveforms using FEM in layered composites and sandwich plates is limited in literature. Modelling of such layered plates is challenging as material properties may vary from one layer to another across the plate thickness. In this study, an effort has been made for two-dimensional finite element modelling for predicting AE waveforms in layered composites and sandwich plates using a generalised refined plate theory modelling. Numerical examples are first solved for isotropic and anisotropic plates and validated with the results obtained from 3d FEM. New results are presented for multilayered composites and sandwich plates.

This paper addresses delamination detection of concrete slabs by analyzing vibration data from dynamic tests. Four concrete decks with different delamination sizes were constructed and experimental tests were conducted on these concrete slabs. Traditional peak-picking (PP), frequency domain decomposition (FDD) and stochastic subspace identification (SSI) methods were applied in the modal identification from real measurements of dynamic velocity responses. The modal parameters identified by these identification methods matched very well. The changes in modal frequencies, damping ratios, and mode shapes that were extracted from dynamic measurements were correlated to delamination size and can indicate presence and severity of delamination. Finite element (FE) models of reinforced concrete plates with different delamination sizes and locations were established. The modal parameters computed from FE models were compared to those obtained from real measurements and FE models were validated.KeywordsMode ShapeDynamic TestConcrete SlabModal IdentificationConcrete DeckThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Modal analysis has developed into a major technology for the study of structural dynamics in the past several decades. Through it, complex structural dynamics phenomena can be represented in terms of structural invariants, i.e., the modal parameters: natural frequencies, damping ratios and mode shapes. Operational Modal Analysis (OMA) deals with the estimation of modal parameters on vibration data measured for operational conditions, when the excitation on the structure is not measured. In this work, OMA is performed on a wind turbine blade undergoing wind tunnel testing. These tests included different wind speeds, pitch angles and also different health conditions of the blade, where masses of different magnitude were fixed to the blade on different locations to emulate damage conditions. In order to monitor the modal parameters across multiple days of varied tests in the wind tunnel, the Polymax modal parameter estimator was implemented, coupled with an Automated Modal Analysis methodology. This methodology included an automatic modal parameter selection technique, using a Machine Learning (ML) clustering algorithm, coupled with a modal tracking procedure which applied statistical thresholds on the modal parameters’ values. The tracking procedure searches for modes similar to the ones calculated for healthy conditions. The results show how the modal parameters of the wind turbine blade vary with the different measured conditions in the wind tunnel. Moreover, a damage detection methodology is implemented to differentiate between the healthy and damaged conditions on the blade, by leveraging an anomaly detection algorithm using the Multivariate Gaussian Distribution (MGD). This algorithm takes as input the modal parameters calculated by the previous Automated Modal Analysis methodology and detects statistical deviations among them which could indicate the presence of damage. All steps of this work contribute to developing an automatic framework able to detect damages on a wind turbine blade, and therefore perform Structural Health Monitoring (SHM) for different operational conditions.