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Journal articles
Unsupervised clustering for building a learning database of acoustic emission signals to identify damage mechanisms in unidirectional laminates
Abstract : This work aims at developing a new clustering method of acoustic emission (AE) signals, called Incremental Clustering (IC), induced by damage mechanisms of glass fibre reinforced composite materials. The advantage of this method over other methods from literature is the capability to identify the signals carrying information and to provide the types of damage mechanisms without using additional expertise. To apply the method based on actual data, several specimens of glass fibre reinforced epoxy composites have been manufactured and subjected to specific mechanical tests. The developed method was compared to the k-means method that is extensively used to classify the AE signals. The reliability of the learning database was checked by the performance evaluation of the k Nearest Neighbours’ (kNN) classifiers. The kNN classifiers were trained by the training dataset and evaluated by the test dataset. The area under the receiver operating characteristic curves (AUC) was used as a criterion for evaluating the performance of classifiers. From specific mechanical tests, the IC method presented more advantages to successfully classify the AE signals and build a labelled learning database than the k-means method. The chronology of appearance of different damage mechanisms demonstrated the effectiveness of the incremental method. The powerful performance of the supervised method, characterized by values of AUC greater than 0.9 for each damage type, confirmed the reliability of the obtained learning database.
https://hal.univ-reims.fr/hal-02497629
Contributor : Daniel Scida <>
Submitted on : Tuesday, March 3, 2020 - 6:05:18 PM
Last modification on : Wednesday, March 4, 2020 - 1:34:15 AM
Contributor : Daniel Scida <>
Submitted on : Tuesday, March 3, 2020 - 6:05:18 PM
Last modification on : Wednesday, March 4, 2020 - 1:34:15 AM