Structural Health Monitoring (SHM) has been embraced as an effective method in the aerospace industry. However, one of the main problems hindering SHM for practical usages is the reliable evaluation of damages under in-service conditions that introduce various uncertainties and difficulties for effectively interpreting SHM signals. The Hidden Markov Model (HMM)-based method has been proved to be potential for improving the reliability of damage evaluation under in-service conditions since it can explicitly model the uncertainties during damage state transition and SHM process. Nonetheless, traditional HMM-based damage evaluation methods need sufficient prior data of several discrete damage levels for training. These prior data come from historical experiments or simulations, which may not be sufficient for a new target structure. In this paper, a Guided Wave (GW)-health HMM damage evaluation method with an on-line calibration strategy is proposed to realize quantitative evaluation of damage propagation under time-varying conditions. At the off-line stage, a health HMM is constructed with the prior GW-SHM data collected under the structural healthy state. The likelihood probability of the on-line monitored GW-SHM feature sequence belonging to the prior health HMM is calculated for detecting the damage initiation. A new health-HMM is trained with the health data from the current target structure once the damage is found, replacing the prior health HMM. On this basis, a calibration strategy is conducted to calibrate the likelihood probability to the damage size for quantitative damage evaluation under time-varying conditions in real-time. Finally, the proposed method is validated on the fatigue test of a full-scale aircraft under an actual flight spectrum. The fatigue crack is reliably evaluated, and the maximum error of the evaluated crack length is 0.5 mm.

结构健康监测 (SHM) 已被视为航空航天工业中的一种有效方法。然而，阻碍 SHM 实际使用的主要问题之一是在使用条件下对损坏的可靠评估，这为有效解释 SHM 信号引入了各种不确定性和困难。基于隐马尔可夫模型 (HMM) 的方法已被证明具有提高服役条件下损伤评估可靠性的潜力，因为它可以对损伤状态转换和 SHM 过程中的不确定性进行明确建模。尽管如此，传统的基于 HMM 的损伤评估方法需要足够的几个离散损伤级别的先验数据进行训练。这些先前的数据来自历史实验或模拟，对于新的目标结构可能不够。在本文中，提出了一种具有在线校准策略的导波（GW）-健康HMM损伤评估方法，以实现时变条件下损伤传播的定量评估。在离线阶段，使用在结构健康状态下收集的先前 GW-SHM 数据构建健康 HMM。计算在线监测到的GW-SHM特征序列属于先前健康HMM的似然概率，用于检测损伤起始。一旦发现损坏，就会使用来自当前目标结构的健康数据训练一个新的健康 HMM，取代之前的健康 HMM。在此基础上，提出一种标定策略，将似然概率标定为损伤大小，进行时变条件下实时定量损伤评估。最后，所提出的方法在实际飞行频谱下的全尺寸飞机的疲劳试验中得到了验证。疲劳裂纹得到可靠评价，评价的裂纹长度最大误差为0.5mm。