Multi-sensor image matching based on salient edges has broad prospect in applications,but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges,a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means(PFCM) kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means(FCM) clustering is sensitive to noises and possibility C-means(PCM) clustering tends to find identical clusters.On this basis,a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition,the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.
The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring.Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction.The effectiveness of the proposed method is verified through simulation signal and experiment data.
