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

Abstract

Identification Noise signal is one of the challenging problems in the health monitoring of bridge structure using acoustic emission monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common method in identifying of the signals from the defect of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focus on the AE noise signal from a bridge in operation state and other specific loading state, which is diagnosed in the hardware filtering technology, spatial identification and 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 signal. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90 % and 100 % respectively. The trained network is used to test183 sample signals, the defect signal detection accuracy reaches 76 % and 78.8 %, therefore, the noise signal filtering effect is significantly improved.

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

“…Traditional bridge health monitoring in the United States is mostly based on the use of strain gages, accelerometers, and displacement sensors. A review by Rizzo and Enshaeian [ 10 ] showed that other devices include, but are not limited to, tiltmeters [ 11 , 12 ], weigh-in motion sensors [ 13 ], non-contact displacement measurements [ 14 ], corrosion sensors [ 15 ], and acoustic emission [ 16 , 17 ].…”