Wind energy is one of the most important renewable energy sources and many countries are predicted to increase wind energy portion of their whole national energy supply to about twenty percent in the next decade. One potential obstacle in the use of wind turbines to harvest wind energy is the maintenance of the wind turbine blades. The blades are a crucial and costly part of a wind turbine and over their service life can suffer from factors such as material degradation and fatigue, which can limit their effectiveness and safety. Thus, the ability to detect damage in wind turbine blades is of great significance for planning maintenance and continued operation of the wind turbine. This paper presents a review of recent research and development in the field of damage detection for wind turbine blades. Specifically, this paper reviews frequently employed sensors including fiber optic and piezoelectric sensors, and four promising damage detection methods, namely, transmittance function, wave propagation, impedance and vibration based methods. As a note towards the future development trend for wind turbine sensing systems, the necessity for wireless sensing and energy harvesting is briefly presented. Finally, existing problems and promising research efforts for online damage detection of turbine blades are discussed.
Concrete-encased composite structure is a type of structure that takes the advantages of both steel and concrete materials, showing improved strength, ductility, and fire resistance compared to traditional reinforced concrete structures. The interface between concrete and steel profiles governs the interaction between these two materials under loading, however, debonding damage between these two materials may lead to severe degradation of the load transferring capacity which will affect the structural performance significantly. In this paper, the electro-mechanical impedance (EMI) technique using piezoceramic transducers was experimentally investigated to detect the bond-slip occurrence of the concrete-encased composite structure. The root-meansquare deviation is used to quantify the variations of the impedance signatures due to the presence of the bond-slip damage. In order to verify the validity of the proposed method, finite element model analysis was performed to simulate the behavior of concrete-steel debonding based on a 3D finite element concrete-steel bond model. The computed impedance signatures from the numerical results are compared with the results obtained from the experimental study, and both the numerical and experimental studies verify the proposed EMI method to detect bond slip of a concrete-encased composite structure.
Desirable properties of carbon fiber-reinforced plastic (CFRP) composites include their high strength, high rigidity, light weight, corrosion free, and fatigue resistance. CFRP composites are popularly applied in bridge engineering structures, but the causes of fatigue damage in CFRP bridges have not been thoroughly investigated. We adopt acoustic emission (AE) technology to monitor fatigue damage and failure of CFRP bridge cables. The relationship between AE signal characteristics and CFRP cable fatigue damage, as well as the pattern of AE signals during a fatigue test, is investigated. Results show that the failure models exhibit matrix and fiber-matrix interface failures at the initial stage of fatigue testing, followed by delamination and fiber rupture. The b value, Kurtosis index, and RA value based on AE characteristic parameters are proposed to describe the different damage stage failure modes. Finally, the failure types of AE waveform are extracted and analyzed using wavelet transformation. The AE technique proved to be a potential means for evaluating the fatigue damage characteristics of CFRP cables.
Summary
Stress corrosion is a major failure type of prestressed steel strands damage. Currently, no effective monitoring method exists. This paper is an analysis of the acoustic emission (AE) characteristic signal from the stress corrosion damage to prestressed steel strands using the ant colony optimization and self‐organizing feature mapping. First, AE characteristic signals at the different stages of the stress corrosion were obtained through the stress corrosion experiments on prestressed steel strands, which can primarily present the corrosion mechanism and different corrosion sources. Subsequently, the ant colony optimization was applied to analyze the AE characteristic signals of stress corrosion. This resulted in the identification of the four main types of AE sources of stress corrosion on prestressed steel strands. The AE ant colony optimization cluster analysis, based on the principal component analysis technology, can separate the four types of damage sources totally and judge the evolution process of corrosion damage and broken wires signal easily. Finally, the self‐organizing feature mapping neural network technology applied to the pattern recognition of stress corrosion on prestressed steel strands. The AE characteristic parameter distribution of different clusters can be realized.
A new method is presented to predict milling forces synthetically. Firstly, the 3D simulation model of the milling process is established using the arbitrary Lagrangian-Eulerian finite element method. And the simulated accuracy is calibrated by milling tests. Then the simulation model is taken as a virtual milling test system to replace extensive real milling experiments. Secondly, the specific cutting coefficients in the mechanistic milling forces model are identified by the support vector regression method using the training sample generated from the established virtual milling test system. Lastly, this methodology was validated by the slot milling operation of 2024-T3 aluminum sheets. The results show that this new approach can dramatically eliminate the experimental machining work and achieve good estimation accuracy.
Abstract:This study identified depths of artificial pitting corrosion on the galvanized steel wires, frequently used in bridge cables, based on the time reversal method (TRM). Specifically, the multimode longitudinal ultrasonic guided waves are excited in terms of characteristics of radical distribution of the normalized average energy flow density (NAPFD) in a wire. Furthermore, the complex defect scattered signals are difficult to interpret, which are attributed to multimode, multipath and dispersion, but are considered to enhance the focused energy through the TRM while the different depths of defect are explicitly identified by the normalized amplitudes of reconstructed wave packets. Finally, in contrast to the traditional monitoring approach relying on the amplitude of defect echo, the proposed method in this study is demonstrated to have a higher sensitivity to recognize the progressive increase of corrosion depth.
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