Abstract
The vibration signal of rolling bearing often has the characteristics of strong noise, nonlinearity and non-stationary, so the accurate fault feature information cannot be obtained directly from the measured vibration signal. Therefore, a fault pattern recognition method for rolling bearing based on complete ensemble local mean decomposition with adaptive noise (CELMDAN) and fuzzy entropy is deeply studied. Firstly, the reason of modal aliasing existing in local mean decomposition (LMD) method is explained. According to the previous methods for modal aliasing processed in other methods, CELMDAN method is proposed. The experiment proves that the proposed CELMDAN method can better handle the vibration signals with nonlinear and non-stationary. Then, the principle and properties of the fuzzy entropy are introduced in detail, and the fault feature of rolling bearing can be reflected. Finally, extreme learning machine (ELM) is introduced as the pattern recognition method based on the effective fault feature of rolling bearing. Combined with the verification of experimental signal, it is proved that the proposed method can extract the fault features of rolling bearing accurately and effectively, and the fault pattern recognition of rolling bearing can be realized.
Highlights
Rolling bearing is widely used in the rotating machinery such as rail vehicle, lifting equipment and so on [1]
This paper introduces a bearing fault pattern recognition method based on CELMDAN and fuzzy entropy
Further from the perspective of pattern recognition, the fuzzy entropy is defined as the sensitive feature of the rolling bearing vibration signal
Summary
Rolling bearing is widely used in the rotating machinery such as rail vehicle, lifting equipment and so on [1]. A FAULT PATTERN RECOGNITION METHOD FOR ROLLING BEARING BASED ON CELMDAN AND FUZZY ENTROPY. When the mechanical vibration signal is disturbed by the impact of the fault, a new signal component is added to the original signal, and the complexity and stability of the entire signal change This corresponds to the generation of new patterns in the definition of entropy. Fuzzy entropy [11] is a typical and effective feature which can be used as the measurement value that reflects the complexity of time series, so it can be used as the sensitive feature of vibration signals of rolling bearing. In this paper, the vibration signal is decomposed by CELMDAN, the fuzzy entropy values of effective PF components are extracted to construct the training and testing samples. The fault pattern recognition of rolling bearing is performed through the trained ELM [16]
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