基于贝叶斯网络的起重机专家系统

Expert System of Crane Based on Bayesian Networks

提出了一种改进当前某大型起重机上的一个基于专家规则知识库的专家系统的方法。改进后的系统保留原有的基于规则库的诊断方式,同时也能运用专家经验构建出来的贝叶斯网络对出现故障进行诊断,诊断出所有可能导致故障发生的原因并以概率度量每个原因发生的可能性。系统采用了因果调查问卷和概率刻度方法对贝叶斯网络进行初始化,同时运用最大似然估计和最大后验估计法则对贝叶斯网络的概率刻度表(CPT)进行学习修正。通过比较改进前和改进后专家系统对同一故障的诊断结果,可以确定改进后的专家系统能更快更准的定位故障原因。

This paper proposes a method to improve Knowledge Base Expert System （KBES） of Some Large Crane, grounded on Expert Rules. On detecting failures of the Large Crane, the improved system can diagnose almost all possible reasons for failures, and measure the occurrence probability of each. The diagnosis process is supported by reasoning, according to Bayesian Network, constructed by Expert Experience. Another feature of this system is adopting a method to initialize Bayesian Network and learn Bayesian Network CPT. By comparison, applications of conventional and improved expert system to failure diagnosis presented in this paper, illustrate that the latter can identify the cause of failure more promptly and accurately.