Entropy-Based Technique for Denoising of Acoustic Emission Signals

The acoustic emission (AE) method has been successfully applied for the Structural Health Monitoring (SHM) of various industrial and civil infrastruc-tures in recent years. In AE, the signal Time of Arrival (ToA) is considered a key parameter for precise localization of a growing defect on a sensorized structure. In this paper, we present an entropy-based filtering approach to improve the signal ToA estimation, w.r.t the commonly used Akaike Infor-mation Criterion (AIC), in noisy environments. The proposed method con-sists of a low-power filtering approach based on coarsening the input data using the Crutchfield-Packard algorithm and calculating the local (instanta-neous) entropy. In the present study we demonstrate that the local entropy of the noise component of a digital signal is significantly lower than that related to the useful (informative) part. As a result, the approach permits filtering to the noise component by selecting a proper threshold value. The proposed strategy has been verified on experimental guided wave data acquired on an aluminum plate used to pinpoint the AE source. The entropy approach demonstrates an overreaching precision in the final localization of the targets under significant noise levels compared to the classical AIC.