The aerospace engine test bench is a key equipment for verifying engine reliability, and its health status assessment is of great significance for ensuring the safe operation of the engine. The gas circuit system of the engine test bench has the characteristics of complex and variable fault modes, strong correlation between multi-point and multimodal sensing information, etc. Moreover, there are issues such as uneven distribution of collected health status samples, high signal noise, human resource waste caused by manual monitoring of the operating status of the gas pipeline system, and high false alarm rates. To this end, a health assessment model for test benches based on adaptive reconstruction of phase space and support for high-order tensor machines is proposed. This method first involves designing stability criterion for E1(m) to achieve adaptive phase space reconstruction of the gas path system. Secondly, tensors are used to characterize the multi-point and multimodal data of the pneumatic system. Then, a high-order tensor machine is used to mine the multi-source sensor correlation information and fault modes in tensor samples, achieving a health status assessment of the test bench pneumatic system. Finally, the proposed method is compared with the support vector machine, decision tree and plain Bayesian algorithms based on the actual test data from the engine test bench of a China National Aviation Corporation (CNAC). The results show that the proposed method has a good evaluation capability in a weak data environment, with an overall evaluation accuracy of 89.7%, and the accuracy drop is kept within 8% in an extremely weak data environment.