Conventional signal decomposition methods often rely on static time-domain indicators for filter design, overlooking the periodic frequency-domain structure of signals. This drawback conceals the spectral allocation of fault-related frequencies and prevents the comprehensive capture of diagnostic information. To address this issue, this article proposes v -Ramanujan spectral signal-to-noise ratio ( v-RSSNR )-driven impact spectrum aware mode decomposition (ISAMD), a method that introduces the v-RSSNR as a dynamic optimization criterion. During each iteration, the v -RSSNR index dynamically adjusts the generalized envelope exponent v , thereby adaptively guiding the update of filter parameters toward its optimal configuration. This enables continuous refinement of the filter’s response to enhance periodic impulse components in the frequency domain. Furthermore, a hybrid criterion combining cross-correlation and v-RSSNR is employed to eliminate redundant components, ensuring that the resulting modes retain highly concentrated periodic impact information. Experimental results based on both simulated signals and real rolling bearing fault data demonstrate that ISAMD achieves superior performance in extracting weak fault features under noisy conditions, confirming its effectiveness as an adaptive and robust decomposition framework for mechanical fault diagnosis.
Li et al. (Wed,) studied this question.