EMAE-Based Rail Structural Health Monitoring Using Double-Layer Signal Processing and Spectrum Information Entropy

Rails play an essential role in railway transportation, supporting the movement of various types of trains. Due to the high-frequency and high-intensity loads, as well as harsh operating environment, rails are susceptible to cracking or even fracturing. Among existing rail structural health monitoring (RSHM) methods, the advanced ones often rely on signal-driven deep learning algorithms, necessitating substantial computational time and extensive preliminary information for effective model training. Moreover, crack-related signals with low amplitudes are easily submerged in the complex interference noise environments. Although some noise reduction solutions have been reported, the RSHM results often obtain certain discrepancies from the actual outcomes. To address above issues, this paper presents an alternative RSHM method based on electromagnetic acoustic emission (EMAE) technology. The proposed method uses a double-layer signal processing (DLSP) algorithm and a novel health monitoring index, called spectrum information entropy (SIE). It can monitor the degradation state of rails accurately and quantitatively. In this method, the DLSP algorithm is utilized to identify EMAE signals from the original dataset, which contains complex and diverse interference noise signals. In addition, the SIE is extracted from the obtained EMAE signals to perform the RSHM. Experimental results validate the accuracy and simplicity of the proposed method.