For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density(PSD) data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method(FNM) is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%. However, only standard power spectral density (PSD) data can be obtained at the stage of flight test. Thus, those conventional evaluation methods that can not be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method (FNM) is proposed which combines the advantages of fuzzy theory and norm theory. which the probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, Compared with statistical methods, FNM has superiority for analyzing expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.