| Fault diagnosis and maintenance decision is one of the key techniques for weapons' fast fault diagnosis and maintenance. It is very important for improving combat readiness rate and rebirth of battle effectiveness, and guaranteeing task success. It can considerably reduce the cost of maintenance and support. With so much anfractuous and coupling correlation, many uncertainty factors and much uncertainty information in complex devices, the fault diagnosis and maintenance decision is very difficult. The researchers in this field always try their best in investigating decision models and decision algorithms, which can quickly fuse various data related to device's fault diagnosis, and effectively handle the uncertainty knowledge.Bayesian Network (BN) is one of the most effective theoretical models for uncertainty knowledge expression and reasoning. It can be applied to decision with various dependent factors. BN is a directed acyclic graph with network structure, which is intuitionistic and easy understanding. It can handle multi-information expression, data fusion and bi-directional parallel reasoning. The ability to colligate the prior information and the current information makes the inference much more accurate and believable. So it is a better choice to use BN for complex device's fault diagnosis.Supported by the National Defense Preparatory Research Projects, Research on Weapon's Fast Fault Diagnosis Technique Based on Data Fusion, this dissertation takes the BN as a diagnostic decision model, and investigates decision methods with the objective of fast and low-cost diagnosis under complicated situations. After introducing the theoretic basis of the BN, it points out several problems of BN fault diagnosis and maintenance decision methods, and proposes a diagnostic Bayesian Network (DBN) model based on the fault hypothesis-observation-maintenance operation nodes. It deeply investigates several key techniques including knowledge expression of DBN, model construction of DBN, and diagnostic decision algorithms based on DBN. Also it presents the designment and realization method of fault diagnosis and maintenance decision system based on DBN. The main points can be summarized as follows.1. The main difficulties faced by fault diagnosis of complex devices are analyzed, and the major limits of existed fault diagnosis decision models and methods are summarized. Then the fault diagnosis decision method based on DBN is put forward, and several advantages and main problems of this method are pointed out.2. After expatiating the probability theoretic basis, it analyzes some issues including BN inference, and BN learning. A DBN structure consisting of fault hypothesis nodes, observation nodes, and repair nodes is brought forward, which combines the general BN and the requirements of fault diagnosis and maintenance decision. The mathematic description and knowledge inscapes of DBN are set forth at last.3. There exist many difficulties for knowledge expression of complex devices' fault diagnosis. Through importing object oriented knowledge expression method, it establishes object oriented diagnostic Bayesian Networks (OODBN) frame and its knowledge operating methods including storage and pick-up, which erects an effective knowledge expression method for complex devices' DBN models.4. In order to handle the construction and reasoning problems, A hierarchy DBN model construction method is established, which is based on TOP-DOWN idea, and which provides a systemic principle for DBN construction of complex devices. Constructing DBN from function models and constructing DBN from fault tree models make it easier for engineers to translate already existed models into DBN models.5. Based on the summary of fault diagnosis decision methods in existence, which use decision theory and BN, fault diagnosis decision method with DBN models is provided. One fault diagnosis decision method under dependent cost is realized through the introduction of appended operation nodes. And one fault diagnosis decision method...
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