Thermal Protection System Damage Diagnosis Method Using Machine Learning Algorithm

The thermal protection system (TPS) for spacecraft is easily damaged by various thermal and mechanical loads, which adversely affects the thermal protection performance of the system. TPS damages diagnosis is one of the most complex and challenging problems for spacecraft structural reliability. The embedded distributed optical fiber sensor can directly reflect the physical field distribution of the structure and then evaluate its health status. One of the challenges of this technique is to accurately extract signal characteristics and reconstruct damage state information. A quantile random forest and self-organizing map (SOM) neural-network-based two-step damage diagnosis framework for thermal protection systems is investigated in this Paper. In this Paper, the combination of physical interpretation and data driving is used to analyze the strain anomaly of the TPS specimen and obtain the damage diagnosis results, including the location, influence area, and categories of damage. First, the abnormal distribution of strain values caused by different types of damage is studied by numerical simulation. Then, the outliers of the experimental strain distribution data are detected by using quantile random forest. A signal features vector is extracted from each signal segment, and the SOM neural network is trained to classify damage. Using the trained model to classify the test set, the accuracy of damage classification is 100%. The experiment result shows great potential for the proposed approach is not only possible to detect the presence of damage, but it is also able to locate and estimate the extent of the damage, which helps one make an appropriate decision on the diagnosis.