Characterization Of Cracks In Vibrating Screen Using Eddy Current C-scan Imaging Technology

It is well known that vibrating screens play a vital role in automated production processes.However,the stress concentration areas such as the shaker girder,side plates,and supporting springs are prone to fatigue cracks due to the long-term high-strength mechanical operation,which is a potential hazard to equipment.Hence,it is necessary to detect vibrating screens regularly.The traditional detection method is highly relied on the experience of inspectors,which mean it is lack of systematic detection process and not reliable.To overcome this,this paper proposes an eddy current C-scan imaging detection method for cracks in vibrating screens.This method achieves the goal of quantitative detection by using simulated analysis and experimental measurements.The main work of this paper is as follows:Chapter two built the simplified finite element model of vibrating screen.Meantime,the finite element plate models of different depth cracks were established by using the software Ansys Maxwell.After that,the influence of cracks with different depths on the response signal of the eddy current probe was simulated and analyzed.All of those provide important guidance for optimizing the parameters of the defect eddy current detection system.Chapter three exploited the eddy current testing system.The system contains three parts: eddy current testing equipment,three-dimensional motion control platform,and upper computer software.And the system can detect the process automatic,which can provide support for the subsequent experimental research.Chapter four achieved automatic identification and classification optimization of cracks.Firstly,by using the eddy current test system to C-scan images of surface cracks and sub-surface cracks,the defective signals of test piece were obtained and C-scan images of cracks were plotted.Then,to achieve defects identifying automatically,the defect recognition algorithm based on SVM was proposed and PSO was used to optimize the parameters of the model.Finally,the features of crack signals which were in the defects identifying process were exacted to optimize identification accuracy.The PCA analysis approach was used to exact the features of cracks signals.The final results show that the latter's classification accuracy is preferable to the former.Chapter five studied the detection methods for crack area,perimeter and length and achieved quantitative detection of crack profiles.Before the quantitative detection,the effects of the three nearest interpolation methods which are bilinear and bicubic interpolation methods on the crack C-scan images were studied.The results showed that the bicubic interpolation method showed the best performance and then the method was utilized to improve the quality of crack C-scan images.Quantitativedetection of cracks was achieved by performing the gray scale and finalization processing on the crack image,a geometric profile of the crack is obtained.Take into account this,the area and perimeters of the crack are calculated.Finally,based on the area and perimeter of the crack,the partial least squares are used.Multiplication determines the crack length.This work provides a theoretical basis for the visualization and quantitative detection of fatigue cracks in the vibrating screen,and has certain theoretical and application values.