Multi-Entropy Feature Extraction With LSTM Networks for Acoustic Emission-Based Railway Crack Localization - Popphon Laon; Supavee Pourbunthidkul; Praphaporn Rattan; Tanawit Sahavisit; Wara Suwansin; Pattharin Wichittrakarn; Pattarapong Phasukkit; Nongluck Houngkamhang (IEEE Access, 2025)
Railway infrastructure security is contingent upon the prompt identification of structural anomalies within steel tracks. This research establishes a framework that merges acoustic emission (AE) sensing with advanced machine learning for prompt fracture identification and location. To improve the quality of Raw AE signals, they are first cleaned using Kalman filtering to tackle environmental disturbances and ambiguous readings. The characteristics that arise from entropy comprising approximate entropy, Shannon entropy, and dimensional entropy are derived to delineate the temporal patterns of fracture-induced emissions. Density-based spatial clustering of applications with noise (DBSCAN) is applied to remove outliers during preprocessing. Long short-term memory (LSTM) networks classify crack locations into three anatomical regions: rail head, web, and foot. Experimental validation with 3,000 labeled AE signals (1,000 per class) under laboratory conditions, the full pipeline that integrates Kalman filtering, entropy features, DBSCAN, and an LSTM classifier attains an accuracy of 98.83%. With an entropy-based variant, the accuracy drops to 96.67%, confirming the incremental value of temporal denoising and outlier rejection. While using mel-frequency cepstral coefficient (MFCC) baseline achieves 97.67% accuracy, a deep neural network (DNN) trained on Kalman-filtered, entropy-based, and DBSCAN-processed inputs reaches 96.33% accuracy, underscoring the advantages of temporal modeling for AE. A GRU using the same Kalman-filtered, entropy-based, and DBSCAN-processed inputs, achieves 97.67% accuracy, but trails the LSTM overall. When deployed on actual railway lines with a mobile inspection platform, the system maintained robust performance, correctly identifying 84.67% of cracks at 3 km/h and 80.67% at 5 km/h. The LSTM configuration consistently outperformed all alternative approaches, including the entropy-only variant, MFCC-based method, DNN classifier, and GRU model. This confirms the LSTM’s enhanced capability to capture the temporal dynamics of AE signals, establishing it as the most effective framework for AE-based crack localization in railway structural health monitoring.
Keywords: Railway Engineering and Dynamics; Infrastructure Maintenance and Monitoring; Structural Health Monitoring Techniques