Characterization,identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material

Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional tests of mechanical property can hardly meet this requirement.Design/methodology/approach-In this study the acoustic emission(AE)technology is applied in the tensile tests of the gearbox housing material of an high-speed rail(HSR)train,during which the acoustic signatures are acquired for parameter analysis.Afterward,the support vector machine(SVM)classifier is introduced to identify and classify the characteristic parameters extracted,on which basis the SVM is improved and the weighted support vector machine(WSVM)method is applied to effectively reduce the misidentification of the SVM classifier.Through the study of the law of relations between the characteristic values and the tensile life,a degradation model of the gearbox housing material amid tensile is built.Findings-The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process,and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%.The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.Originality/value-The results of this study provide new concepts for the life prediction of tensile samples,and more further tests should be conducted to verify the conclusion of this research.

Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine

Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.

Effect of TGO on the tensile failure behavior of thermal barrier coatings

Thermally grown oxide(TGO)may be generated in thermal barrier coatings(TBCs)after high-temperature oxidation.TGO increases the internal stress of the coatings,leading to the spalling of the coatings.Scanning electron microscopy and energy-dispersive spectroscopy were used to investigate the growth characteristics,microstructure,and composition of TGO after high-temperature oxidation for 0,10,30,and 50 h,and the results were systematically compared.Acoustic emission(AE)signals and the strain on the coating surface under static load were measured with AE technology and digital image correlation.Results showed that TGO gradually grew and thickened with the increase in oxidation time.The thickened TGO had preferential multi-cracks at the interface of TGO and the bond layer and delayed the strain on the surface of the coating under tensile load.TGO growth resulted in the generation of pores at the interface between the TGO and bond layer.The pores produced by TGO under tensile load delayed the generation of surface cracks and thus prolonged the failure time of TBCs.