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Large Equipment System Acceptance (such as reliability and accuracy, etc.) is a complicated system engineering. In order to check the performance of large equipment, multi-perspective and more-approach tests are adopted to get a variety of test information. These information are related to many aspects, such as the information in different phases of design, development, pilot, production and application phase, test information of products in different levels (systems, subsystems, components) and the history information of test-related products. These different but still interrelated information have brought a great more reference to analysis and assessment of large-scale equipment, and meanwhile those uncertain information would brought more risk to the assessment of decision-making. How to integrate these information of multiple sources effectively to make an objective evaluation on the performance of large-scale equipments has been a great challenge to the engineering researchers. For example, in assessing the reliability of weapons systems, the cost of system-level testing is often too much which limits the number of test times. In that condition the test information of various equipment and subsystems are urged to be fully utilized. Similarly, in order to improve the practical accuracy of INS (inertial navigation system), a series of checking from the test phase to application phase such as ground calibration tests, vehicle-loaded tests, aircraft-loaded tests and missile-loaded tests, are requisite and the outcome should function to the error coefficients estimation. In the circumstance of sufficient test data, the classical approach of comprehensive assessment to reliability has been widely used; while in contrast when test data are insufficient and moreover they present multi-stage and multi-level properties, the classical approach is in effectiveness challenge. With the development of computer technology and improvement of Bayes methods, especially the emergence of MCMC (Markov chain Monte Carlo) methods and WinBUGS (Bayesian inference Using Gibbs Sampling) software, the Bayesian network is more and more popularized in the application of multi-source information fusion [1~3]. The Bayesian network is a causal network, which is used as an inference engine for the calculation of beliefs or probability of events given the observations of other events in the same network. It does not only make good use of model information and sample data, but also integrates the unknown parameters in the overall distribution of information. Besides, it 8

Method of Multi-Source Testing Information Fusion Based on Bayesian Network