Clustering-Based Quantitative Evaluation Using Acoustic Emission Waveforms for Corrosion Detection

Farrukh Hassan, Ahmad Kamil Mahmood, Lukman Ab. Rahim, Syed Muslim Jameel, Abdul Saboor, Mohamed Rimsan


Acoustic emission technique has been used commonly now a day for mechanical diagnostics. These events need to be discriminated against due to various kinds of damages in composite materials. Continuous wavelets transform (CWT) has been used to generate scalograms. Structural similarity index measure (SSIM) generates the similarity index table of these scalograms. The performance of various clustering techniques, such as k-means, k-medoids, the Hierarchical clustering was evaluated by several validity indices such as Dunn’s Index, Davies Bouldin Index, Silhouette Index, Calinski-Harabasz index, and the execution time. We use acoustic emission data to verify the application of these techniques. The results demonstrated that the execution time of the k-means clustering is the shortest, and silhouette, Davies Bouldin, and Calinski-Harabasz give the best possible value for (K=2).


Keywords: continuous wavelet transform, structural similarity index, acoustic emission, clustering techniques, cluster validation indices.

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