Abstract
Multiscale entropy (MSE), as a complexity measurement method of time series, has been widely used to extract the fault information hidden in machinery vibration signals. However, the insufficient coarse graining in MSE will result in fault pattern information missing and the sample entropy used in MSE at larger factors will fluctuate heavily. Combining fractal theory and fuzzy entropy, the time shift multiscale fuzzy entropy (TSMFE) is put forward and applied to the complexity analysis of time series for enhancing the performance of MSE. Then TSMFE is used to extract the nonlinear fault features from vibration signals of rolling bearing. By combining TSMFE with the Laplacian support vector machine (LapSVM), which only needs very few marked samples for classification training, a new intelligent fault diagnosis method for rolling bearing is proposed. Also the proposed method is applied to the experiment data analysis of rolling bearing by comparing with the existing methods and the analysis results show that the proposed fault diagnosis method can effectively identify different states of rolling bearing and get the highest recognition rate among the existing methods.
Highlights
Rolling bearing is a typical part for realizing the rotational motion and prone to broken in the rotating machinery due to the complexity of working conditions, which will result in the machinery operation unstable or even unexpected accidents
When the rolling bearing works with local faults, the non-linearity and non-stationarity are obviously expressed in vibration signals, which makes the traditional linear analysis methods loss their functions in analyzing these kinds of signals [1,2,3]
Compared with traditional linear analysis methods, the nonlinear dynamic methods can effectively extract the nonlinear fault features from vibration signals of rolling bearings and promote the performance on fault identification and detection of rolling bearing [12]
Summary
Rolling bearing is a typical part for realizing the rotational motion and prone to broken in the rotating machinery due to the complexity of working conditions, which will result in the machinery operation unstable or even unexpected accidents. Zhong et al in [14] and the results indicated that the feature information of intrinsic mode functions (IMFs) can be effectively extracted by calculating sample entropy and applied to fault diagnosis of rolling bearing. By combing the FuzzyEn proposed by Chen et al [19] and inspired by the idea of time shift way [20], a new complexity measuring method of time series called time shift multiscale fuzzy entropy (TSMFE) is proposed in this paper to overcome the above limitations of MSE. TSMFE is compared with MSE and time shift sample entropy (TSME) by simulation signal analysis and is applied to the complexity feature extraction of vibration signals from faulty rolling bearings.
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