Thermoelastic stress analysis using arrays of small, low-cost detectors has the potential to be used in structural health monitoring. However, evaluation of the collected data is challenging using traditional methods, due to the lower resolution of these sensors, and the complex loading conditions experienced. An alternative method has been developed, using image decomposition to generate feature vectors which characterize the uncalibrated map of the magnitude of the thermoelastic effect. Thermal data have been collected using a state-of-the-art photovoltaic effect detector and lower cost, lower thermal resolution microbolometer detectors, during crack propagation induced by both constant amplitude and frequency loading, and by idealized flight cycles. The Euclidean distance calculated between the feature vectors of the initial and current state can be used to indicate the presence of damage. Cracks of the order of 1 mm in length can be detected and tracked, with an increase in the rate of change of the Euclidean distance indicating the onset of critical crack propagation. The differential feature vector method therefore represents a substantial advance in technology for monitoring the initiation and propagation of cracks in structures, both in structural testing and in-service using low-cost sensors.

使用小型低成本检测器阵列进行的热弹性应力分析有可能用于结构健康监测。但是，由于这些传感器的分辨率较低，并且经历了复杂的加载条件，因此使用传统方法评估收集的数据具有挑战性。已经开发出一种替代方法，该方法使用图像分解来生成特征向量，这些特征向量表征了未经校准的热弹性效应幅度图。在恒定振幅和频率负载以及理想的飞行周期引起的裂纹扩展过程中，已经使用最先进的光伏效应检测器和成本更低，热分辨率较低的微辐射热检测器收集了热数据。在初始状态和当前状态的特征向量之间计算出的欧几里得距离可用于指示损坏的存在。长度为1毫米量级的裂纹可以被检测和跟踪，随着欧几里德距离的变化率的增加，表明出现了严重的裂纹扩展。因此，微分特征向量法代表了在结构测试和使用低成本传感器的服务中，用于监视结构中裂纹产生和传播的技术的重大进步。