This paper proposed a novel diagnosis algorithm based on Hurst exponent and BP neural network to detect carbide anvil fault in synthetic diamond industry. Firstly, a sort of preprocessing algorithm is proposed, which uses the sliding window and energy threshold method to separate the pulse from initial continuous signal. Then, some characteristic parameters which are based on Hurst exponent are extracted from the separated pulse signal. These characteristic parameters are used to construct fault characteristic vectors. Finally, the BP neural network model was established for fault recognition. Experimental results show that the proposed fault detection method has high recognition rate of 96.7%.
In order to solve problem of high time complexity of support vector data description (SVDD) in training process, a low complexity SVDD algorithm is proposed by introducing statistical information grid (STING).The algorithm applies STING division to sample set space using support vector distribution characteristics and kernel distances between samples and sphere’s centre. Based on position information of sample points, it rejects non-support vectors and obtained simplified sample set. The results show that proposed method can reduce training scale and time without decreasing classification accuracy.
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