Data processing and fusion for monitoring and fatigue-induced damage detection in a welded steel specimen

Le résumé fourni par la source

In the field of structural health monitoring (SHM) of steel structures, early detection of fatigue damage in welded joints is still considered a crucial issue and represents a major challenge. This study explores the use of data fusion from multiple sensor types to improve the early identification of fatigue-related damage in welded assemblies. Standardized fatigue tests were conducted on steel specimens equipped with diverse sensors. Crack detection was performed consistently across all sensors using a unified methodological framework, with the Mahalanobis distance applied to assess detection performance. Individual sensor data were analyzed to detect and localize cracks, followed by a feature-level data fusion approach employing mathematical and statistical tools, including the Mahalanobis distance, to enhance detection earliness. Two fusion strategies were evaluated: intra-sensor fusion (within the same sensor type) and inter-sensor fusion (across different sensor types). Results indicate that intra-sensor fusion significantly enhances early detection, whereas inter-sensor fusion provides limited improvements, highlighting the need for further optimization.

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