Abstract: Reliable fault diagnosis of rolling bearings is an important issue for the normal operation\nof many rotating machines. Information about the structure dynamics is always hidden in the\nvibration response of the bearings, and it is often very difficult to extract them correctly due to the\nnonlinear/chaotic nature of the vibration signal. This paper proposes a new feature extraction model\nof vibration signals for bearing fault diagnosis by employing a recently-developed concept in graph\ntheory, the visibility graph (VG). The VG approach is used to convert the vibration signals into\na binary matrix. We extract 15 VG features from the binary matrix by using the network analysis\nand image processing methods. The three global VG features are proposed based on the complex\nnetwork theory to describe the global characteristics of the binary matrix. The 12 local VG features\nare proposed based on the texture analysis method of images, Gaussian Markov random fields,\nto describe the local characteristics of the binary matrix. The feature selection algorithm is applied\nto select the VG feature subsets with the best performance. Experimental results are shown for\nthe Case Western Reserve University Bearing Data. The efficiency of the visibility graph feature\nmodel is verified by the higher diagnosis accuracy compared to the statistical and wavelet package\nfeature model. The VG features can be used to recognize the fault of rolling bearings under variable\nworking conditions.
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