Civil aircraft engine thrust reversers are easy to have overspeed faults when they are being deployed, therefore this condition is very important for flight safety and maintenance work efficiency. The low sampling frequency, about 1Hz, of onboard data recording systems, for example WQAR, together with external environment interferences such as sudden strong winds, therefore causes a quite large number of wrong and missed alarms in fault data. Therefore, the labeled data that can be obtained are not only few in number but also have low quality. In this article, a fault diagnosis framework which is based on semi-supervised learning is put forward to solve the problems of high labeling expenses and fault diagnosis under the condition of small samples. This framework makes use of a small amount of labeled data and a large amount of unlabeled data. It uses Semi-Supervised Support Vector Machines, Semi-Supervised Neural Networks, and Semi-Supervised Random Forests for dynamic self- training to realize classification of three kinds of states: “Normal State,” “Low-Speed Thrust Reverser Deployment Exceedance,” and “Medium-Speed Thrust Reverser Deployment Exceedance”. The experiment outcomes show that the Semi-Supervised Neural Network model which uses self-training method has the highest classification accuracy on the test set, achieving 94.8%. Further robustness analysis, such as label noise and feature noise disturbance experiments, indicates that this model displays the smallest performance lowering under noisy environments, hence it represents the best overall performance. This research gives an effective method for the diagnosis of engine thrust reverser function problems in an effective and strong way.