Abstract
The useful information extracted from fracture images is the most fundamental problem of quantitative analysis and intelligent diagnosis of metal fracture. The pattern recognition or classification is the critical issue of failure analysis of metal fracture. In this paper, combining wavelet transform, kurtosis with relevance vector machine (RVM), a new recognition method based on wavelet kurtosis and RVM, which is named wavelet kurtosis-RVM, is proposed. In the proposed method, wavelet kurtosis is used as a feature vector, and RVM as a classifier. The proposed method has been successfully applied to the recognition of fracture images. The proposed method is also compared with the wavelet entropy-RVM recognition method and wavelet kurtosis-SVM recognition method. The experiment result shows that the proposed method is very effective. Compared with the Wavelet entropy-RVM recognition method, Wavelet kurtosis is more sensitive to the texture change of metal fracture and suitable for feature extraction of metal fracture. Compared with the Wavelet kurtosis-SVM recognition method, The proposed method and Wavelet kurtosis-SVM recognition method have the same good recognition rate. However, in the recognition speed, the Wavelet kurtosis-RVM recognition method is obviously superior to the Wavelet kurtosis-SVM recognition method, especially in the increase of training samples.
Highlights
In the many failure analysis, fracture analysis is a very important technology
Combining wavelet kurtosis with the relevance vector machine (RVM), a recognition method of metal fracture image based on wavelet kurtosis-RVM is proposed
In this paper, a new feature extraction method of metal fracture image based on wavelet kurtosis is proposed
Summary
In the many failure analysis, fracture analysis is a very important technology. Fracture is formed through the different stage, i.e. formation, expansion and final break [1]. Compared with the traditional feature vector of fracture image, such as L1 norm and energy etc, Kurtosis is more sensitive to the texture feature. In this paper, combining wavelet transform with kurtosis, a new concept, which is named wavelet kurtosis, is proposed and used to extract the texture features of metal fracture image. The proposed wavelet kurtosis is compared with wavelet entropy in the feature of metal fracture image. Relevance vector machine (RVM), which is proposed by Tipping[6], is a probabilistic sparse kernel model identical in functional form to the SVM. Combining wavelet kurtosis with the RVM, a recognition method of metal fracture image based on wavelet kurtosis-RVM is proposed. The proposed method is compared with the wavelet entropyRVM and wavelet kurtosis-SVM recognition method.
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