A diagnosis based on Bayesian theory requires knowledge of the a priori and conditional probabilities of the states of the system being diagnosed. The a priori probabilities are frequently provided nowadays by the manufacturers of these systems. In turn, the probabilities of conditional observations are, as a rule, not available. The question arises as to whether and under what conditions it is possible to substitute conditional probabilities with some aggregate obtainable on the grounds of fuzzy logic. This article responds to this question by proposing a hybrid approach with novelty characteristics in both theoretical and practical terms. In the initial phase of the deliberations, it was concluded that the fundamental difference between Bayesian and fuzzy approaches is that the fuzzy approach considers the uncertainty and lack of precision of observations but overlooks the frequency of observations, and the opposite is true of the Bayesian approach. It therefore seems reasonable to seek the hybridization of both methods so that the Bayesian approach carrying the information regarding the subjective probabilities of faults can be applied in practice. To this end, it has been shown that the probability of a conditional observation can be estimated by calculating the degree of truth of the premise for that observation in the state-specific fuzzy rule. The reminder is devoted to presenting numerical and simulation examples illustrating and verifying the proposed approach.

基于贝叶斯理论的诊断需要了解被诊断系统状态的先验概率和条件概率。这些系统的制造商如今经常提供先验概率。反过来，条件观察的概率通常是不可用的。出现的问题是，是否以及在什么条件下可以用一些基于模糊逻辑可获得的集合来代替条件概率。本文通过提出一种在理论和实践方面都具有新颖性特征的混合方法来回答这个问题。在审议的初始阶段，得出的结论是，贝叶斯方法与模糊方法的根本区别在于，模糊方法考虑了观察的不确定性和精度的不足，而忽略了观察的频率，而贝叶斯方法恰恰相反。因此，寻求两种方法的混合似乎是合理的，以便携带有关故障主观概率的信息的贝叶斯方法可以在实践中应用。为此，已经表明，可以通过计算特定状态模糊规则中该观察的前提的真实度来估计条件观察的概率。提醒专门用于展示说明和验证所提出方法的数值和模拟示例。而贝叶斯方法则相反。因此，寻求两种方法的混合似乎是合理的，以便携带有关故障主观概率的信息的贝叶斯方法可以在实践中应用。为此，已经表明，可以通过计算特定状态模糊规则中该观察的前提的真实度来估计条件观察的概率。提醒专门用于展示说明和验证所提出方法的数值和模拟示例。而贝叶斯方法则相反。因此，寻求两种方法的混合似乎是合理的，以便携带有关故障主观概率的信息的贝叶斯方法可以在实践中应用。为此，已经表明，可以通过计算特定状态模糊规则中该观察的前提的真实度来估计条件观察的概率。提醒专门用于展示说明和验证所提出方法的数值和模拟示例。已经表明，可以通过计算特定状态模糊规则中该观察的前提的真实程度来估计条件观察的概率。提醒专门用于展示说明和验证所提出方法的数值和模拟示例。已经表明，可以通过计算特定状态模糊规则中该观察的前提的真实程度来估计条件观察的概率。提醒专门用于展示说明和验证所提出方法的数值和模拟示例。