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本文运用Bayes定理分析了各种故障发生的先验概率对在已知征兆下判断故障的准确性的影响情况;论述了使用模糊概率描述征兆、故障、征兆与故障之间的映射关系等的方法;在将模糊概率和节约覆盖集理论集成的基础上,提出了基于浅知识的新的诊断推理方法.该方法在一定程度上减小了获取有关概率知识的难度,并对概率值的定义误差有一定的容错能力.
Bayes theorem is used in this paper to analyze the influence of prior probability of various faults on the accuracy of the fault under the known symptoms. The method of using fuzzy probability to describe the relationship between the symptoms, faults, the signs and the faults is discussed. Based on the integration of fuzzy probability and saving cover theory, a new diagnostic reasoning method based on shallow knowledge is proposed, which reduces the difficulty of acquiring knowledge about probability to a certain extent and defines the definition error of probability value A certain degree of fault tolerance.