A baseline-free method for in-service crack detection under complex loading conditions

Fatigue crack initiation and propagation pose critical challenges to structural integrity assessment, particularly in safety-critical applications where reliable in-service monitoring is essential. Previous studies have demonstrated the feasibility of baseline-free crack detection using higher-order harmonic parameters, specifically the second harmonic parameter (β′) and the third harmonic parameter (γ′). While these parameters showed the potential for online in-service crack detection, their fluctuations and the dependence of the first stage on sensor bonding conditions, thereby limiting robustness and interpretability. To address these limitations, the present study introduces an alternative feature based on the root mean square (RMS) value of time-domain signals for baseline-free in-service crack monitoring. owing to its direct reflection of the signal content, the RMS features demonstrates greater stability and reliability compared with harmonic-based parameters. The Dynamic Piecewise Linear (DPL) method was employed to analyze RMS data obtained from fatigue experiments conducted on multiple specimens. Results reveal that the RMS change can be clearly divided into two stages, with the critical transition point closely coinciding with the experimentally observed crack initiation. Furthermore, the proposed approach successfully identified cracks smaller than 2 mm, yielding consistent detection outcomes across different specimens and sensor configurations. Most importantly, the RMS-based method exhibited insensitivity to sensor bonding conditions, thereby addressing one of the primary shortcomings of harmonic-based approaches. This study confirms the feasibility of using RMS as a robust and interpretable feature for baseline-free online crack detection. The observed two-stage evolution and the consistency of detection across varying test conditions provide a solid foundation for the practical implementation of structural health monitoring (SHM) systems in engineering applications.