Dual-Control Inference Diffusion Model via Multi-Sensor High-Frequency Signal for Space Transportation Engine Anomaly Detection

Liquid Rocket Engine, as the key power device of the space transportation system, the anomaly detection of operation status is the key to its reliable operation. However, in the face of multi-sensor high-frequency monitoring signals under extreme operating conditions, limited by the ability of model data modeling, the existing methods, based on classification and reconstruction strategies, are difficult to further improve the anomaly localization precision. To address the challenges and overcome the limitations of existing methods, this paper proposes a Dual-control Inference Diffusion Model (DIDM), which reconstructs and inferences on specified sensor samples to achieve accurate anomaly detection. The reverse diffusion inference process is controlled by the channel condition and mask prior, combined with two loss functions for alternating training, which enables inference for samples from specified sensors at specific moments. We evaluate the model based on the static ignition test data of a certain type of LRE. The results show that DIDM outperforms the state-of-the-art methods in terms of detection accuracy, which demonstrates the effectiveness and superiority of DIDM. Furthermore, by combining the error distributions of the inference results, we can achieve a more accurate location of anomaly in the time and frequency domains, which could increase the efficiency of rocket launches and air and space transportation, and enhance the potential of the academic results for industrial applications.