Abstract: Targeting the status monitoring and fault diagnosis requirements for reusable rocket engines, addressing the issues of non-stationary vibration signals and difficulty in extracting effective fault features, a method for state monitoring based on variational mode decomposition（VMD） and fuzzy C-means（FCM） clustering is proposed. The optimized VMD algorithm was adopted to adaptively decompose the vibration signal into multiple intrinsic mode functions（IMF）, and key IMF components were selected based on the weighted correlation sample entropy maximum criterion; the time-domain and frequency-domain feature dimensionality reduction of key IMF components using t-distributed stochastic neighbor embedding（t-SNE） was employed to obtain the feature vector matrix, and the fuzzy center means clustering algorithm was used to monitor the working status of the engine. The method was applied to the monitoring of the working status of the turbopump, and the results showed that it can extract key features of vibration signals and accurately identify the working status of the turbopump. The recognition accuracy of the test set reached 92. 50%, providing theoretical support for status monitoring and fault diagnosis of rocket engines.