###### Abstract  
Targeting reliability assessment problems with a small sample size and large amount of zero-failure data which appear in the life assessment tests of aero-engine compressor disks, a reliability assessment method and simulation test method were developed by fusing numerical simulation and zero-failure data based on the life reliability assessment method for zero-failure data. The developed method was verified by numerical examples, and further applied in the reliability analysis of compressor disks by combining the test and simulation results. The results indicated that fusing the test data with the simulation data increased the amount of information, improved the life prediction accuracy, and increased the lower confidence limit of the reliable life by 6.02% compared with the  
test, and Kolmogorov-Smirnov (_K-S_) test. This method needs to be supported by a large number of real experimental data and is not suitable for the case of less experimental data. Parametric test introduces the distribution function. The form of the distribution of the two matrices is assumed, and statistical theories, e.g., hypothesis testing or significance testing, are used to consider the consistency of the distribution function of the simulation results and the reference data. At this point, the problem is transformed into determining whether the parameters of the two distributions are equal, parametric methods such as the _F_-test, the _t_-test, confidence interval estimation, and the Cramer-von Mises test can be used to evaluate the simulation results. However, this method does not have enough reason to accept the null hypothesis when it cannot reject it. Therefore, [PERSON] [4] proposed the concept of confidence distribution of matrix eigenvalues based on \"small probability principle\" and established the confidence test theory, which can well meet the requirements of engineering test. In order to improve the accuracy of reliability, the multi-source information fusion technology has been introduced into the reliability analysis, which can be divided into three categories as [5]: (1) the weighted-average method, (2) the neural-network method, and (3) the Bayes method. The weighted-average method estimates the reliability parameter by using certain weights to combine the data from different sources. In this method, it is crucial to determine the weights of the data from different sources. Numerous researchers have adopted the multi-source data fusion method, which is a type of weighted-average method, for reliability assessment of aircraft clutches, aero-engine rotors, turbofan engines, etc., [6; 7; 8; 9]. However, the weighted average method has high requirements on the amount of data and has the disadvantages of strong subjectivity under small samples, so its application is limited. The neural network method [10; 11; 12; 13] directly establishes the connection between the raw data and the output results by using multi-layer perceptron. The neurons in the multi-layer perceptron can be nonlinear functions, which can simulate simple nonlinear data. To ensure the accuracy of the output results, numerous samples must be used for training in the early stage. Thus, the amount of data required for the neural-network method is large, making the method unsuitable for small-sample size situations. The Bayes method uses the reliability data from other sources as a priori information, and the posterior distribution of the reliability parameters is obtained by using the Bayes formula together with the current source data to achieve the fusion of multi-source reliability information [14]. Many researchers have proposed the data fusion process of the Bayes method to combine expert experience, test data, and performance degradation data for product life and reliability assessment [15; 16; 17; 18; 19; 20; 21; 22]. The Bayes criterion has become the mainstream method for multi-source reliability information fusion [16].