Airborne oxygen concentrator based on molecular sieve bed pressure swing adsorption principle is the core component of aircraft life support system, which can provide oxygen for pilots during flight. The degradation analysis of airborne oxygen concentrator can realize fault early warning, which is of great significance for the condition based maintenance of airborne oxygen concentrator and the construction of aircraft health management system. The degradation process of airborne oxygen concentrator can be divided into two stages: steady stage and accelerated degradation stage. However, due to the uncertainty of degradation mode change point, the transformation of degradation mode is uncertain. Therefore, it is very important to correctly identify the turning point of degradation mode. Oxygen partial pressure is an important parameter to reflect the oxygen production capacity of airborne oxygen concentrator. In this paper, the Shannon entropy of the data is extracted by using the data-driven method. Then the SKF filter is used to process the real-time data samples. The current degradation mode is identified according to the posterior probability between the steady-state degradation filter and the accelerated degradation filter, the recognition results are consistent with the actual situation. Finally, compared with wavelet decomposition and K-means algorithm, the effectiveness of Entropy-SKF algorithm is proved.