Abstract
Signal identification including noise filtering and reduction of acquired signals is needed to achieve efficient and accurate data interpretation for remote acoustic emission (AE) monitoring of in-service steel bridges. Noise filtering may ensure that genuine hits from crack growth are involved in the estimation of fatigue damage and remaining fatigue life. Reduction of the data quantity is desirable for the sensing system to conserve energy in the data transmission and processing procedures. Identification and categorization of acquired signals is a promising approach to effectively filter and reduce AE data in the application of bridge monitoring. In this study an investigation on waveform features (time domain and frequency domain) and relevant filters is carried out using the results from AE monitored fatigue tests. It is verified that duration-amplitude (D-A) filters are effective to discriminate against noise for results of steel fatigue tests. The study is helpful to find an appropriate AE data filtering protocol for field implementations.
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