Cluster Analysis of Signals of Storage Tank Acoustic Emission Testing

Cluster analysis is an advanced unsupervised pattern recognition technology, which can classify data and reveal the data internal structure without prior knowledge. In this paper, the application of floating threshold value to calculate the characteristic parameters of acoustic emission test common waveform data is used as input vector clustering algorithm by optimizing K means clustering calculation. The featuring vector by floating threshold calculation has more acoustic emission waveform characters, obtaining good clustering effect. By analysis of acoustic emission test data of storage tank, the results show that K means clustering can determine different sound signals from different sound sources and propagation paths, affording excellent de-noise effect and effectively improving acoustic emission test accuracy of tank bottom.