Identification of fiber tensile fracture acoustic emission signal based on principal component analysis

For problems of acoustic emission signal of fiber tensile fracture such as nonstationarity and high overlap between signal characteristics, a model was presented for the feature extraction of acoustic emission signal and fiber type diagnosis. The model can be used to identify the type of fibers stretched. Firstly, different kinds of tensile fracture acoustic emission signals were preprocessed and decomposed by wavelet transform and ensemble empirical mode decomposition (EEMD). Then, the frequency characteristics were extracted by the principal component analysis (PCA). Finally, least squares support vector machine (LSSVM) was used to classify the characteristic frequency of the fiber stretched. Results show that the principal component analysis method can further select the eigenvector sets of the two kinds of fiber tensile fracture acoustic emission signals, and make the signal characteristics from high dimensional to low dimensional. At the same time, the correlation between the features is reduced, and the accuracy of recognition of fiber tensile fracture of AE signal is improved. The EEMD-PCA-LSSVM model has a total recognition rate of 96% for the acoustic emission signals of PMIA (poly-m-phenylene isophthalamide) and high performance vinylon fiber. Copyright No content may be reproduced or abridged without authorization.