Xuefeng Chen

Composite Damage Detection Based on Redundant Second-Generation Wavelet Transform and Fractal Dimension Tomography Algorithm of Lamb Wave

In the purpose of achieving composite structure damage identification and localization of structural health monitoring, a denoising algorithm of redundant second-generation wavelet transform considering neighboring coefficients is selected as the best solution from 18 denoising schemes performed in this paper. Through introducing fractal dimension as a damage-sensitive feature and adopting the probabilistic reconstruction algorithm, the damage status on the composite panel could be identified and located as tomography maps. The practicability of the presented approach is validated in the experiment operating in the composite damage monitoring system.

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.

Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis

Vibration monitoring is one of the most effective ways for bearing fault diagnosis, and a challenge is how to accurately estimate bearing fault signals from noisy vibration signals. In this paper, a nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms. Furthermore, we introduce a k-sparsity strategy for the adaptive selection of the regularization parameter. The main advantage over conventional filtering methods is that GMC can better preserve the bearing fault signal while reducing the interference of noise and other components; thus, it can significantly improve the estimation accuracy of the bearing fault signal. A simulation study and two run-to-failure experiments verify the effectiveness of GMC in the diagnosis of localized faults in rolling bearings, and the comparison studies show that GMC provides more accurate estimation results than L1-norm regularization and spectral kurtosis.

Composite damage identification based on lamb wave and redundant second generation wavelet

Aiming at achieving composite materials damage identification of Structure Health Monitoring (SHM), an preprocessing algorithm of Redundant Second Generation Wavelet (RSGW) considering neighboring coefficients is introduced, which can denoises Lamb wave signals from noise flooding and structure complexity conditions for damage features extraction and further calculations. The practicability is validated through experiment operated in composite damage monitoring system.

An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet

De-noising and extraction of weak signals are crucial to fault prognostics, and the wavelet transform has been widely used in signal de-noising. In this paper, a new method, which combines the Adaptive Redundant Second-Generation Wavelet (ARSGW) and the Hilbert transform, is proposed. The ARSGW is applied to reveal the transient components of the signal in time domain clearly. Then the Hilbert transform is used to extract fault features of rolling bearing from the wavelet packets. The analysis results of the vibration signals from the experiment and the machine tool spindle show that the proposed method can detect the faults of the rolling bearing effectively.

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.

The Condition Monitoring and Performance Evaluating of Digital Manufacturing Process

Spindle assembly is one of the most important components of digital manufacturing equipments. It is key problem to monitor and evaluate its performance to assure the normal process. Aiming at the bearings which damage most easily in spindle system, a new time-frequency analysis method called S transform is studied, time-frequency distribution of vibration signals collected from spindle is obtained, and a singular value decomposition method is employed to condense the time-frequency matrix data so that the fault features can be extracted quantitatively. Simulation and experimental studies have demonstrated that the proposed method may identify the running state of spindle bearing accurately. Thus a new technique for the evaluation of spindle serving performance of digital manufacturing equipments is provided.

Composite laminates damage detection based on basis pursuit denoising algorithm

When Lamb waves propagates in composite laminates, the direction-dependent property parameters of the composite laminates lead to different propagation velocities, which is known as the anisotropy nature. It makes the problem of damage detection more difficult. A sparse reconstruction based damage imaging method considering the anisotropy nature of composite laminates is presented in this paper. A propagation model of Lamb waves considering the propagation directions is introduced to build a wavefield dictionary. Then the problem of damage detection is formulated as a sparse reconstruction problem, which can be solved by basis pursuit denoising algorithm. Finally, experiments with simulated damages are performed and the results validate the effectiveness of the method.