1. College of Mechanical Engineering,Taiyuan University of Technology,Taiyuan　 030024,China;2. Post-doctoral Scientific Research Station,Shanxi Coking Coal Group Co. ,Ltd. ,Taiyuan　 030022,China

针对传统提升机故障诊断系统中知识获取困难、知识表示单一且故障诊断推理方法自适应能力弱从而导致诊断推理结果不稳定等问题,研究了面向知识工程的提升机智能故障诊断方法。重点针对提升机故障诊断过程中的三大关键科学问题,即知识获取、知识表示和知识推理技术进行了深入研究:提出了基于融合差别矩阵和属性重要度的提升机故障诊断规则知识获取方法,为提升机故障诊断提供了数据基础;构建本体知识库,提出了基于OWL DL的故障诊断知识表示方法和基于SWRL的故障诊断规则知识表示方法,实现了提升机系统结构及诊断知识的集成;对本体知识进行了概率扩展,提出了基于本体和贝叶斯网络的不确定性知识融合推理方法,提高了推理的效率和准确率。开发了面向知识工程的智能故障诊断系统,通过实例验证和企业应用证明了该方法的可行性和准确性。

To overcome the instability of the diagnostic reasoning results caused by the difficulty in knowledge acquisi- tion,the single knowledge representation,and the poor self-adaptation ability of fault diagnosis reasoning method in tra- ditional hoist fault diagnosis systems,the hoist fault diagnosis method based on knowledge engineering is investigated. Fault diagnostic rule knowledge acquisition methods based on improved attribute importance is proposed,and it pro- vides a data basis for hoist fault diagnosis. The mine hoist fault diagnostic ontology knowledge base is constructed and the fault diagnostic ontology knowledge representation methods based on OWL DL and fault diagnostic rule knowledge representation methods based on SWRL are proposed,and the hoist system structure and the diagnosis knowledge inte- gration are implemented. The probability of the ontology knowledge is extended,and a new fault diagnosis uncertainty knowledge reasoning method is proposed,which are based on ontology and Bayesian. Based on the theory and method above,the fault monitoring and diagnosis system of the mine hoist is developed,and the method is proved to be feasible and reliabile.

山西省科技重大专项资助项目(20111101040)；山西省青年基金资助项目(2014021024-2)；

1. College of Mechanical Engineering,Taiyuan University of Technology,Taiyuan　 030024,China;2. Post-doctoral Scientific Research Station,Shanxi Coking Coal Group Co. ,Ltd. ,Taiyuan　 030022,China)