Zhi-Bo Yang

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.

Analysis of Laminated Plates and Shells Using B-Spline Wavelet on Interval Finite Element

Composite materials, with characteristics of light weight and high strength, are useful in manufacturing. Therefore, precise design and analysis is the first key procedure in composite applications, improper analysis or use of composite materials may cause serious failures. In this paper, wavelet finite element method (WFEM) based on B-spline wavelet on the interval (BSWI) is constructed for precise analysis of laminated plates and shells, which gives a guidance in design and application of composite structures. First, FEM formulations are derived from the generalized potential energy function based on the generalized variational principle and virtual work principle. Then, BSWI scaling functions are used as interpolation function to discretize the solving displacement field variables. At the same time, transformation matrix is constructed and used to translate the meaningless wavelet coefficients into physical space. At last, the static analysis results can be obtained by solving the FEM formulations. Due to the excellent features of BSWI, such as multiresolution, multiscale, localization and excellent numerical approximation characteristics etc., BSWI-based FEM can achieve accurate and efficient analysis by comparing with traditional methods. In the end, the effectiveness of the constructed BSWI WFEM is verified through several numerical examples.

The effects of thermal residual stresses and interfacial properties on the transverse behaviors of fiber composites with different microstructures

Abstract This study presents a new micromechanical model to investigate the effects of thermal residual stresses and interfacial properties on the transverse behaviors of SiC/Ti composites with different microstructures. In this model, the fiber-matrix interface is modeled by the bilinear cohesive zone model. The interface model is introduced into the generalized method of cells, which has the advantage of computational accuracy and efficiency. At the same time, the generalized method of cells is extended to consider thermal residual stresses within the fiber and matrix phases. Thermal residual stresses are found to have a significant influence on the transverse behaviors of the composites. Compared with the perfect interface, the transverse behaviors of the composites with weak interface bonding are much lower. Moreover, with the increase of fiber fraction, the stiffness of the composites increases before debonding occurs while the saturation stress decreases. The predicted results using the circular fiber model and considering thermal residual stresses are more consistent with the experimental values compared with the results using the square or elliptical fiber model. When the stress concentration factor is considered and the interface is weakly bonding, the strength predictions are much better than the results using the perfect bonding.

Sparse representation based on spectral kurtosis for incipient bearing fault diagnosis

The bearing fault, generating harmful vibrations, is one of the main causes of machine breakdowns. Therefore, performing bearing fault diagnosis is a key point to improve the reliability of the mechanical systems and reduce the corresponding operating expense. Recently, more and more studies focus on addressing this problem through detection transient signal by means of sparse representation (SR) theory. Although tremendous progress has been made, one important drawback remains to be solved: in the early stage of bearing failure, the incipient impact signal is relatively weak, which is hard to detect due to other mechanical components harmonic signals and interference of noise. In order to solve this problem, spectral kurtosis (SK), a popular tool to detect non-stationary signal, is introduced as a pre-procedure of the transient signal sparse representation. A novel sparse representation based on spectral kurtosis method is proposed in this work, namely the SKSR. SKSR utilizes the advantages of both of the methods: it is possible to choose the best matching of the atomic signal with the failure signal structure feature to gain sparse representation and efficiently extract transient signal from strong noise. The effectiveness of the proposed method is verified by the numerically simulations and lab experiments. The results show that the presented method is efficient for the title problem.