It is common on space vehicles to have thermal insulation adhesively bonded to a metal structure. A typical defect in such structures is an interlayer disbond, which may occur either between the insulation and the metal substructure or between the layers of multilayer thermal insulation. One-sided thermal nondestructive testing (TNDT) using surface optical heating, such as Xenon flash or quartz tube, may detect disbonds if the thermal insulation thickness does not exceed a few millimeters and disbonds are not very small. In thicker insulation, the effectiveness of the inspection can be improved by using electrical induction to heat the metal base. In both cases, thermal excitation can be areal heating, which is heat projected over an area by a stationary heat source, or scanned linear heating (SLH), which is a linear heater scanned across the test subject. In the latter, either the linear heater is moved across a stationary test subject, or the linear heater is stationary and the test subject is moved. The SLH method usually provides a higher inspection rate (inspected area unit time). In this research, both the theoretical and experimental features of both optical and induction heating have been investigated and compared in the application to non-metallic insulation adhesively bonded to a metal structure. The effectiveness of using neural networks (NN) for characterizing defects has also been studied to demonstrate that optimal NN training should involve 4–5 points selected in defect areas close to non-defect areas, and the NN input data should be prepared by applying the known technique of Thermographic Signal Reconstruction (TSR). Since SLH provides more uniform heating, it provides higher quality IR thermograms than those obtained from areal (flash) heating and this improves the detectability of defects in thermal insulation to a depth of 4–6 mm. Other advantages of SLH for TNDT testing are (1) an inspection rate that is twice as high as an area heating technique and (2) a better potential for fully automated (robotic) testing.

在航天器上通常具有将隔热材料粘合到金属结构上的功能。这种结构中的典型缺陷是层间粘结，该层间粘结可能发生在绝缘体和金属子结构之间，也可能发生在多层绝热层之间。如果隔热层的厚度不超过几毫米，并且粘合力不是很小，则使用氙气闪光灯或石英管等表面光学加热进行的单面热非破坏性测试（TNDT）可能会检测到粘合力。在较厚的绝缘层中，可以通过使用感应加热金属底座来提高检查的有效性。在这两种情况下，热激励都可以是面加热，它是由固定热源投射到一个区域上的热量，也可以是扫描线性加热（SLH），这是在整个测试对象上扫描的线性加热器。在后者中，或者线性加热器在固定的测试对象上移动，或者线性加热器是固定的并且在测试对象上移动。SLH方法通常提供更高的检查率（检查区域单位时间）。在这项研究中，对光学加热和感应加热的理论和实验特性都进行了研究，并比较了其在粘结到金属结构的非金属绝缘材料中的应用。还已经研究了使用神经网络（NN）表征缺陷的有效性，以证明最佳NN训练应包括在靠近非缺陷区域的缺陷区域中选择的4–5个点，并且应通过应用神经网络来准备NN输入数据。热成像信号重建（TSR）的已知技术。由于SLH可以提供更均匀的加热，因此它提供的质量红外热分析图要比面热（闪蒸）获得的质量更好，并且可以将隔热缺陷的可检测性提高到4–6 mm的深度。SLH用于TNDT测试的其他优点是：（1）检验率是区域供热技术的两倍，并且（2）全自动（机器人）测试的潜力更大。