The real-time prelaunch condition monitoring of the flight control systems for rocket requires a fast response speed, which results in missing signals of partial sampling points if it is unmet. The topological inconsistency of sampling points caused by missing signals increases the difficulty in extracting hidden fault features and reduces the accuracy of condition monitoring. This study proposes a missing-signal tolerant condition monitoring method via multiscale features and domain adaptation. The appropriate size of the sliding window is selected by the fuzzy entropy index (FEI) to improve the real-time response speed of the proposed method. The spatial features of sampling points in different topologies are aggregated by a graph neural network (GNN). A multiscale feature extractor (MFE) with a channel attention mechanism is designed to extract multiscale temporal features. The domain generalization performance of the monitoring model for limited information is improved through unsupervised domain-adversarial training of neural networks to avoid domain shifts caused by missing signals. A test dataset with missing signals is obtained from the virtual signals generated by a ground test simulator. The experimental results of this dataset verify the feasibility and effectiveness of the proposed method.