虽然对基于模型的诊断存在一系列不同的逻辑定义，但值得庆幸的是存在一个统一的抽象定义，它概括了以往的不同定义。本文在该定义基础上提出了基于模型的中心诊断的概念。通过刻划基于模型的中心诊断过程，论证了基于模型的中心诊断与本原蕴含/蕴含式的直接关系，从而将本文的理论结果与ATMS这类算法联系起来。本文进一步指出，对基于一致性中心诊断的刻划仅仅是本文所给出的刻划的一个特殊情形。

在基于模型诊断中，一般使用冲突集的所有极小碰集来表达诊断结果。本文提出一种利用与元素相关联的冲突集个数来计算碰集的新方法，并结合带有终止节点的集合枚举树SE-tree形式化地表达计算过程，来逐步地生成所有的极小碰集。由于在Setree中添加了终止节点，因而能够较大地提高搜索的效率。

Variable elimination algorithm was proposed for inference using Bayesian networks. In this paper, we explore further on simplifying structures of Bayesian networks to reduce computational complexity. We propose the concepts of omissible node and replaceable node, and prove that we could delete the omissible nodes and replace replaceable nodes and their ancestors without affecting inference results using Bayesian networks. In many cases, the network can be simplified by our proposed methods, and therefore, the computational efficiency could be improved in average.

The methods for computing the kernel consistency-based diagnoses and the kernel abductive diagnoses are only suited for the situation when part of the fault behavioral modes of the components are known. The characterization of the kernel model-based diagnosis based on the general causal theory is proposed, which can breakthrough the limitation of the above methods when all behavioral modes of each component are known. Using this method, when observation subsets deduced logically are respectively assigned to be empty or the whole observation set, the kernel consistency-based diagnoses and the kernel abductive diagnoses can deal with all situations. The direct relationship between this diagnostic procedure and the prime implicants/implicates is proved, which links theoretical result with implementation.

Model-based diagnosis is a new intelligent diagnosis technique which can overcome the shortcomings of traditional diagnostic methods. Hierarchical diagnosis is an important method for reducing the complexity of model-based diagnosis. In this paper, hierarchical description of the device to be diagnosed is proposed and the relationships between different abstract levels are pointed out. The soundness of hierarchical diagnosis is also proved: a diagnosis at the abstract level has a corresponding diagnosis at the detailed level. Furthermore, the incompleteness of hierarchical diagnosis is pointed out: a diagnosis at the detailed level may not have a corresponding diagnosis at its abstract level.