Acoustic signal recovering for rubbing in a dual-rotor system based on diffusion probabilistic models

Inverse problems that recovering target data from measured ones remains a challenging task. This study proposed a novel diffusion probabilistic method for the inverse problem that recovers target acoustic data from distorted ones, in particular industrial scenarios. Specifically, the distribution of acoustic signals was incorporated into the forward process where a parameterized score function is introduced. For the reverse process, a novel strategy is proposed to approximate the posterior distribution to recover acoustic signals from distorted ones through a scheduled Langevine Sampling. The effectiveness of this method was validated on a public dataset as well as signals obtained from a realistic dual -rotor test rig. Experimental results indicated that this method achieved an average improvement of 1.42 dB and boosted the performance of existing state-of-the-art studies in the quality of recovered data. This implies that the proposed approach seems proficient in mitigating distortions arising from Gaussian noise encountered in industrial condition diagnosis scenarios.