Fault stands out in contrast: Zero-shot diagnosis method based on dual-level contrastive fusion network for control moment gyroscopes predictive maintenance

Abstract

Control moment gyroscopes (CMGs) are the most common control actuators in spacecraft. Their predictive maintenance is crucial for on-orbit operations. However, due to the scarcity of CMG fault data, constructing a diagnosis system for predictive maintenance with CMGs poses significant challenges. Therefore, a zero-shot fault diagnosis method based on a dual-level contrastive learning fusion network was proposed. First, to address the difficulty in training CMG fault diagnosis models without fault data, a contrastive learning method based on CMG clusters was proposed to extract invariant features from healthy CMGs and achieve zero-shot diagnosis for predictive maintenance. Second, considering the limitations of information from a single sensor, a cross-sensor contrastive learning method was proposed to fuse features from different sensors. Third, to tackle the challenges of extracting weak potential fault features, a dual-level joint training method was introduced to enhance the model’s feature extraction capability. Finally, the proposed method was validated using real dataset collected from CMGs serviced on an in-orbit spacecraft. The results demonstrate that the method can achieve zero-shot fault diagnosis for control moment gyroscopes predictive maintenance. The code is available at https://github.com/IceLRiver/DCF.