Clustering of Acoustic Emission Signals Collected during Tensile Tests on Hydrogenation Reactor Material

摘要

Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in a constrained metal material. And cluster analysis can separate a set of data into several classes that reflect the internal structure of the data. AE waveform contains a wealth of information about AE source, and the traditional parameters can no longer meet the higher demands of AE source identification. In this paper, we worked hard to extract new parameters from three aspects: vector of frequency band energy extracted by wavelet transformation characterizes the frequency distribution of the waveform, waveform Margin factor characterizes shape feature of AE waveform and the amplitude characterizes intensity of AE waveform. We worked on specimens of hydrogenation reactor material 2.25CrlMo, subjected to tensile loading, awaiting damage modes in the material. K-mean clustering based on new parameters was used to analysis the AE signals during the total procedure, and signals of plastic deformation at yield stage, micro-crack signals and crack signals at destructed stage were finally indentified.