As a recently proposed signal decomposition method, spectral denoising random feature decomposition (SDRFD) has been proven feasible in extracting fault components from non-stationary signals induced by actual gear faults. However, the accuracy of SDRFD in frequency band segmentation is susceptible to interference components, and its frequency domain denoising effect is also limited by the fixed threshold. To address these bottleneck issues, this paper proposes a novel spectral editing random mode decomposition (SERMD) method. Initially, SERMD constructs the random feature energy spectrum of the given signal using sparse random feature expansion as a bridge. Following this, by progressively adjusting the amplitude of the spectrum, a spectral editing strategy is developed to finely attenuate noise components in the spectrum and enhance the diversity of the spectrum. Subsequently, a bandwidth-constrained spectral trend segmentation technique is proposed to accurately divide the frequency bands of spectra obtained through spectral editing, thereby ensuring the integrity of frequency band fault information. Finally, an adaptive mode extraction strategy with frequency band compensation is formulated to obtain mode components with rich periodic fault information from segmentation results of different spectra and complete the decomposition of the given signal. The effectiveness of SERMD is verified through the gear fault simulation signal and experimental signals. Moreover, compared to existing prevalent methods, SERMD has more outstanding performance in accurately diagnosing gear faults.
Liu et al. (2026) studied this question.