Abstract
Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear 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 method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.
Highlights
As a main component in almost all types of rotating machine rolling element bearings have been used widely
We introduce a multifractal theory based method, i.e., a generalized multifractal dimension algorithm, to extract dominant characteristic vector of the fault features from bearing vibration signals
A novel approach for rolling element bearing fault diagnosis was proposed based on generalized multifractal dimension algorithm and adaptive gray relation algorithm
Summary
As a main component in almost all types of rotating machine rolling element bearings have been used widely. Nowadays a great number of signal processing methods have been used for rolling bearing fault detection and diagnosis. Some entropy based methods, e.g., approximate entropy (ApEn) [14,15], sample entropy (SampEn) [16], fuzzy entropy (FuzzyEn) [16,17], hierarchical entropy (HE) [13,18] and hierarchical fuzzy entropy (HFE) [13], have been proposed to to extract characteristic vector of the fault features from bearing vibration signals. We introduce a multifractal theory based method, i.e., a generalized multifractal dimension algorithm, to extract dominant characteristic vector of the fault features from bearing vibration signals. The gray relation theory for fault pattern recognition is given briefly, followed by section 4 which presents the proposed bearing fault diagnosis method.
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