Abstract
In this paper, a new gearbox fault identification method was proposed based on mathematical morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by mathematical morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EEMD method. Thirdly, some IMFs containing the most dominant fault information were calculated by the sample entropy for four gearbox conditions. Finally, since the grey relation degree has good classified capacity for small sample pattern identification, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gearbox fault diagnosis. It’s suitable for on-line monitoring and fault diagnosis of gearbox.
Highlights
Gear is a key component usually used in mechanical transmission, for its prominent carrying capacity and reliability
In order to extract fault feature of gearbox, in this paper, a novel approach is proposed based on mathematical morphological filter, ensemble empirical mode decomposition (EEMD) and grey relation degree
To verify good effectiveness in gearbox fault identification, all vibration signals were collected from the experimental testing of gearbox using the accelerometer which was mounted on the outer surface of the bearing case
Summary
Gear is a key component usually used in mechanical transmission, for its prominent carrying capacity and reliability. Enveloping analysis and wavelet package decomposition are commonly used in fault diagnosis as feature extraction methods for gear signal [1]. Sample entropy is a good tool to evaluate complexity of non-linear time series, compared with other existing non-linear dynamic methods It has many good characteristics, such as good resistance of noise interference and closer agreement with theory for data sets with known probabilistic content. Sample entropy displays the property of relative consistency in some situations where approximate entropy does not [7] These performances are suitable for fault extraction in practice. In order to extract fault feature of gearbox, in this paper, a novel approach is proposed based on mathematical morphological filter, EEMD and grey relation degree. The proposed method could extract gear fault feature by EEMD and sample entropy. We identify a different gearbox fault mode by calculating the grey relation degree between the fault sample and standard fault pattern
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