基于语义网的机床故障诊断知识扩展方法研究
首发时间:2018-04-25
摘要:知识获取一直是机床故障诊断专家系统的瓶颈问题。机床设备的故障诊断知识量大且动态性强,机床类型众多,通过研究机床故障诊断知识的扩展方法,可对获取到的故障诊断知识进行高效的收集,提升知识获取的效率。本文将研究机床故障诊断知识的扩展方法与关键技术,提出可动态新增知识的机床故障诊断知识扩展平台框架。本文提出的知识平台可实现构建不同类型机床的故障诊断知识模型和动态新增故障诊断知识实例,实现对获取到的机床故障诊断知识的收集。最后,实验证明,本文提出的扩展方法,能够构建出不同类型机床的故障诊断知识模型和收集获取到的故障诊断知识实例。
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Machine Tool Fault Diagnosis Knowledge Expansion Method Based on Semantic Web
Abstract:Knowledge acquisition has always been a bottleneck problem in machine tool fault diagnosis expert systems. Knowledge of machine tool fault diagnosis has large quantities and strong dynamics by the diversity types of machine tools. By studying the expansion method of machine tool fault diagnosis knowledge, the acquired fault diagnosis knowledge can be efficiently collected and the efficiency of knowledge acquisition can be improved. This paper will study the expansion methods and key technologies of machine tool fault diagnosis knowledge, and propose a framework of machine tool fault diagnosis knowledge expansion platform that can dynamically add new knowledge. The knowledge platform proposed in this paper can construct the knowledge model of fault diagnosis for different types of machine tools and dynamically added fault diagnosis individuals knowledge. Finally, the experiment proves that the expansion method proposed in this paper can construct the fault diagnosis knowledge model of different types of machine tools and collect the acquired fault diagnosis knowledge individuals.
Keywords: fault diagnosis knowledge expansion semantic web ontology
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