Many existing cyclic spectrum analysis methods are difficult to solve the bearing fault diagnosis of multi-information frequency band. Based on this, an improved weighted envelope spectrum (IWES) method is proposed. Firstly, IWES uses the fault information intensity (FII) index to quantify bearing fault information and evaluate bearing fault information of spectral frequency bands in spectral coherence. Secondly, the threshold function is constructed to determine the threshold adaptively, so as to identify the spectrum frequency components with rich fault information in the spectral coherence. Meanwhile, a weight function is designed based on threshold function to eliminate the interference noise components and keep the fault information. Finally, the spectral coherence and weight function are used to generate IWES with multi-band information. The bearing experiments show that the IWES has excellent noise robustness and can accurately extract the bearing fault characteristic frequency.
Ramanujan Fourier mode decomposition (RFMD) obtains components by scanning from low frequency to high frequency, which will cause too many components, and then the fault information in mode components is incomplete. Based on this, the empirical Ramanujan decomposition (ERD) method is proposed in this paper. Firstly, ERD uses the optimized lowest minima technique to segment the spectrum and determine the segmentation boundary and the number of components. Subsequently, ERD constructs the filter banks for filtering and retains the spectral components corresponding to the main frequency band. Finally, the time domain components are recovered by the inverse Ramanujan Fourier transform. To further improve the capability of envelope spectrum (ES), an iterative envelope spectrum (IES) method is proposed. IES enhances the periodic components through iterative envelope to make the fault feature more conspicuous. The analysis results of simulation and experimental signals show that the ERD and IES can extract fault features effectively.
To enhance the periodic impulse component and improve the accuracy of planetary gear fault detection, an enhanced weighted symplectic geometry decomposition based on maximum periodic kurtosis deconvolution (MPKD-EWSGD) is proposed in the paper. On the one hand, MPKD-EWSGD adopts the maximum periodic kurtosis deconvolution (MPKD) method for noise reduction preprocessing to highlight the periodic impulse component. On the other hand, MPKD-EWSGD introduces the periodic impulse intensity (PII) to choose components with fault information, avoiding the disadvantages of denoising methods that use the component energy as the measurement standard. The analysis results of emulation and experimental signals show that MPKD-EWSGD can effectively reduce noise and is an effective method for planetary gearbox fault detection.
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