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Aiming at the nonlinear and nonstationary characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm was proposed in this article. First, an improved generalized fractal box-counting dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, to offer more useful and distinguishing information imaging different bearing health status in comparison with traditional fractal box-counting dimension. After feature extraction by improved generalized fractal box-counting dimension algorithm, an adaptive gray relation algorithm, in which the concept of weight coefficient and adaptive distinguishing coefficient was introduced into the calculation of the relation degree, was employed to fulfill an intelligent bearing fault diagnosis. The experimental results demonstrate that the proposed approach can more effectively and accurately identify different bearing fault types as well as severities compared with the existing intelligent methods, and it can solve the learning problem with an extremely small number of samples.

Study on rolling bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm