Adaptive spectrum segmentation Ramanujan decomposition and its application to gear fault diagnosis

Abstract

Ramanujan Fourier mode decomposition (RFMD) is a novel non-stationary signal decomposition method, which can decompose a complex signal into several components and extract the periodic characteristics of the signal. However, the mode generation method adopted by RFMD does not consider the physical meaning of the component signal, which makes over-decomposition when dealing with real-life gear signals with complex modulation characteristics, thus destroying the integrity of the signal sideband, increasing the difficulty of subsequent analysis, and even losing key fault information. The iterative envelope-segmentation algorithm combines the modulation characteristics of the fault gear signal and divides the original signal into a limited number of dominant frequency bands containing the modulation region in the Fourier spectrum, thereby ensuring that the obtained frequency bands contain rich fault information. Based on the above algorithm, a new adaptive decomposition method is proposed in this paper, which is Adaptive Spectrum Segmentation Ramanujan Decomposition (ASSRD). ASSRD uses fault envelope harmonic noise ratio (FHNR) as theindex to evaluate the fault information content of component signals and uses it to assist theiterative envelopesegmentation algorithm to complete the adaptive segmentation of the Fourier spectrum. Finally, based on the segmentation result, the inverse RFT reconstruction of each frequency band is performed. Thus, the signal is decomposed into a finite number of component signals containing rich fault information. In addition, through the gear simulation signal and the measured gear signal experiment, the ASSRD method is compared with the original RFMD method and the existing ensemble empirical mode decomposition, variational mode decomposition, empirical wavelet transform, and singular spectrum decomposition method, verifying the feasibility and superiority of ASSRD in gear fault diagnosis. The results show that the proposed method can extract the fault information in the gear signal more effectively, and the performance is better than the comparison method.

Data-powered NDE for smart structure manufacturing and maintenance

“…The paper by Huang et al [13] proposed a Ramanujan decomposition method with adaptive spectrum segmentation and applied it to gear fault diagnosis. This approach allows a complex nonstationary signal to be recursively decomposed in the time domain into several simple intrinsic mode functions (IMFs), each IMF being typically an amplitude modulated frequency modulation signal with a compact Fourier spectrum.…”