Abstract
Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.
Highlights
Rolling bearings are a key part of rotary machines and are directly related to machine health status.it is very essential to introduce an effective method to accurately detect potential abnormalities in bearing
The remainder of this paper is organized as follows: Section 2 introduces the related works to illustrate the principles of existing algorithms; Section 3 describes proposed works; Section 4 evaluates the performance of the proposed method for fault diagnosis; Section 5 is the conclusion
To compare the extracted ability of the rolling bearing, the first final intrinsic mode functions (FIMFs) identified by the sample entropy (SE) method, VMD [53] and CELMDAN combining with kurtosis [24] were employed to extract fault information
Summary
Rolling bearings are a key part of rotary machines and are directly related to machine health status. To avoid the drawbacks associated with the use of spurious mode, Marcelo et al [22] proposed a method to extract the k-th IMF by adding an EMD decomposed mode This method may reduce residual noise and obtain more modes with physical meanings, it still has interference in the spurious mode. IMFs into a minimum number of IMFs with physical meanings to enhance CEEMDAN decomposition This method produces new mode function (CIMFs) by combining adjacent IMFs; the FFT is done for each CIMF to enhance the difference features of the frequency component in IMFs; all information can be reflected by PDF estimation. The remainder of this paper is organized as follows: Section 2 introduces the related works to illustrate the principles of existing algorithms; Section 3 describes proposed works (enhancing decomposed algorithm of CEEMDAN and identifying IIM algorithm); Section 4 evaluates the performance of the proposed method for fault diagnosis; Section 5 is the conclusion
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