Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

Abstract

Identification of noise signals is one of the most challenging problems in health monitoring of a bridge structure using acoustic emission (AE) monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common methods used in identifying signals from the defects of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focuses on the AE noise signal from a bridge in an operation state and other specific loading states, which is diagnosed in the hardware filtering technology, spatial identification, and self-organizing map (SOM) neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50%, and can barely filter strong noise signals. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90% and 100%, respectively. The trained network was used to test 183 sample signals, and the defect signal detection accuracy reached 76% and 78.8%; therefore, the noise signal filtering effect is significantly improved.