Abstract
It is very difficult to extract the feature frequency of the vibration signal of the rolling bearing early weak fault and in order to extract its feature frequency quickly and accurately. A method of extracting early weak fault vibration signal feature frequency of the rolling bearing by intrinsic time‐scale decomposition (ITD) and autoregression (AR) minimum entropy deconvolution (MED) is proposed in this paper. Firstly, the original early weak fault vibration signal of the rolling bearing is decomposed by the ITD algorithm to proper rotations (PRs) with fault feature frequency. Then, the sample entropy value of each PR is calculated to find the largest PRs of the sample entropy. Finally, the AR‐MED filtering algorithm is utilized to filter and reduce the noise of the largest PRs of the sample entropy value, and the early weak fault vibration signal feature frequency of the rolling bearing is accurately extracted. The results show that the ITD‐AR‐MED method can extract the early weak fault vibration signal feature frequency of the rolling bearing accurately.
Highlights
Rolling bearings play a pivotal role in modern industry and their applications are very extensive, such as aircraft engines [1, 2], gas turbines [3], landing gear [4], flexible mechanism [5], and wind turbines [6,7,8,9,10,11]
The rolling bearing is very easy to cause damage in a strong vibration environment, and the rolling bearing is very prone to failure in most rotating machinery systems. e failure of the rolling bearing will even cause a catastrophic accident to the rotating machinery system, which will indirectly cause huge economic losses. erefore, it is significant to accurately extract the early weak fault vibration signal feature frequency of the rolling bearing [12]
The wavelet basis function (WBF) is fixed before the wavelet packet decomposition (WPT) decomposition and the decomposition effect according to the selection of the WBF. erefore, WPT has obvious defects in vibration signal decomposition. e empirical mode decomposition (EMD) and local mode decomposition (LMD) are prone to adverse factors such as endpoint effects and mode mixing [26,27,28,29]. erefore, Frei and Osorio [30] proposed an intrinsic time-scale decomposition (ITD) adaptive signal decomposition algorithm. e ITD algorithm can effectively solve the unfavorable factors between EMD and LMD
Summary
Rolling bearings play a pivotal role in modern industry and their applications are very extensive, such as aircraft engines [1, 2], gas turbines [3], landing gear [4], flexible mechanism [5], and wind turbines [6,7,8,9,10,11]. Erefore, it is significant to accurately extract the early weak fault vibration signal feature frequency of the rolling bearing [12]. Sawalhi et al [36] and Endo and Randall [37] utilized the MED to enhance the impact component of the rolling bearing and gear vibration signal, respectively, and the experiment results proved that this method could effectively enhance the feature frequency. The AR-MED method can eliminate the influence of background noise effectively, which can enhance the feature frequency in the fault signal. This paper studied the problem of extracting early weak fault vibration signal feature frequency of the rolling bearing under complex background noise. Is paper proposes a method for extracting early weak fault vibration signal feature frequency of the rolling bearing based on ITD and AR-MED This paper studied the problem of extracting early weak fault vibration signal feature frequency of the rolling bearing under complex background noise. is paper proposes a method for extracting early weak fault vibration signal feature frequency of the rolling bearing based on ITD and AR-MED
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