Identification of Acoustic Emission Signal of Tank Bottom Corrosion Based on Weighted Fuzzy Clustering
The identification of corrosion acoustic emission signal of tank bottom is the basis of improving detection accuracy. However, under some circumstances, the types of corrosion acoustic emission signal of the tank bottom are unknown, and a weighted fuzzy clustering recognition method was proposed to solve this problem. The main characteristics of the signal were given by acoustic emission detector; the characteristics included rise time, count, energy, duration, amplitude, and average frequency. Aiming at the randomness of clustering initialization, nearest neighbor method was used for optimizing initial clustering. For improving the accuracy of edge data, a weighted fuzzy clustering method was proposed to increase the difference of various acoustic emission signals. The data redistribution method of fuzzy clustering is adjusted with the weighted distance between the gravity and center to substitute the traditional distance and then allocated data to the set which had the minimum weighted distance. This study shows that the veracity of weighted fuzzy clustering increases by about 9 %.
KeywordsPetroleum Covariance Acoustics
This work is supported by the Creative Team Project Foundation of the Education Department of Liaoning Province, China (Grant No. LT2010082).
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