Telemetry data is the important basis for ground operators to judge the status of the on-orbit satellites. Anomaly detection can effectively ensure the reliable operation of satellites in orbit. To deal with the problem of the existing telemetry anomaly detection algorithms, such as the poor discrimination capability of the feature, and loss of decision-making information, a self-ensemble anomaly detection method based on center-constrained contrastive learning was proposed. The method mapped the normal samples to a compact feature distribution by combining contrastive loss and center loss, and then a multi-view and multi-level ensembled feature decision method was used to obtain the anomaly score of the sample. The method improves the adaptability of the model to the complex working conditions of the satellite. The proposed method is robust to noise, and achieves 21.7% improvement of F1 score than that of the state of the art method. The results of the experiment demonstrate the feasibility of the method, which can provide effective support for satellite operation.