基于混合推理机制的故障诊断专家系统

多数故障诊断专家系统采用单一的推理机制,或者基于规则的推理,或者基于事例的推理。而这两种推理机制都各有优缺点,采用单一推理机制会造成诊断的不准确性。论文将基于规则的推理和基于事例的推理相结合,设计了混合推理机制。在此基础上,论文设计了一个既有专家知识库,又有故障事例库,具有自学习能力的故障诊断专家系统(AFDES)。实验结果表明,论文设计的混合推理机制是比较有效的。

Fault Diagnosis Expert System Based on Mixed Reasoning Mechanism

Most fault diagnosis expert system adopted single reasoning mechanism, or rule-based reasoning or case-based reasoning. However, each reasoning mechanism had advantages and disadvantages. The adoption of anyone would lead to inaccurate diagnosis. The paper designed mixed reasoning mechanism based on rule-based reasoning and case-based reasoning. On the base of this, the paper designed a self-learning fault diagnosis expert system, namely, AFDES, which had expert knowledge database and fault case database. The experiments indicate that designed mixed reasoning mechanism is effective.