Abstract
In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition (LCD) and six typical features including relative energy spectrum entropy (LREE), relative singular spectrum entropy (LRSE), two-element multiscale entropy (TMSE), standard deviation (STD), RMS, and root-square amplitude (XR) are extracted and compose the original multidomain feature set. And then, locally preserving projection (LPP) is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP.
Highlights
Rolling element bearings are one of the most important components for carrying heavy loads and providing constant rotational speed in rotating machines [1]
Kurtosis is insensitive to initial damage [3] and it can hardly characterize the slight degradation state exactly. These years, the information entropy theory is widely used in signal processing and fault diagnosis and it develops into different forms of entropy with different properties such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), spatial information entropy (SIE), and fuzzy entropy (FuzzyEn) [4, 5]
In order to make the fusion features gained from locally preserving projection (LPP) dimension reduction distinguish different degradation states better, genetic algorithm (GA) is applied to optimize the kernel space where there are kinds of training samples
Summary
Rolling element bearings are one of the most important components for carrying heavy loads and providing constant rotational speed in rotating machines [1]. Kurtosis is insensitive to initial damage [3] and it can hardly characterize the slight degradation state exactly These years, the information entropy theory is widely used in signal processing and fault diagnosis and it develops into different forms of entropy with different properties such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), spatial information entropy (SIE), and fuzzy entropy (FuzzyEn) [4, 5]. By the neighbor graphs obtained from highdimensional features, LPP algorithm can gain its projection in the low-dimensional space In this way, fusion and reduction of high-dimensional data are achieved. . degradation state recognition is realized by GG clustering and the principle of maximum membership
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