Research on noise reduction method for acoustic emission signals based on Kalman filtering

Based on the Cloud-Edge system architecture proposed by the research team, this article investigates the method of Kalman filtering in acoustic emission signal denoising processing. Acoustic emission (AE) signals are small sounds generated when a material is subjected to stress or deformation, often disturbed by environmental noise, which reduces the clarity and usability of the signal. In order to effectively reduce the impact of noise on AE signals, this paper adopts the Kalman filtering algorithm. By dynamically modeling and estimating the signal, noise suppression and signal recovery are achieved. Through comparative experiments and analysis, it has been proven that the proposed method has significant advantages in noise reduction and signal fidelity of acoustic emission signals, and has good application prospects.