粗糙集理论在农业机械故障诊断中的应用  

Application for Agricultural Mechanical Fault Diagnosis with Rough Set Theory

作　　者：杨雨时[1] 张丽娜[1] 周静[1] 

机构地区：[1]河北农业大学理学院,河北保定071001

出　　处：《农机化研究》2013年第4期249-252,共4页Journal of Agricultural Mechanization Research

基　　金：河北省自然科学基金项目(F2010001044);河北省科技计划项目(12237125D-2);河北农业大学非生命基金项目(FS201008)

摘　　要：粗糙集理论是一种处理模糊和不确定知识的工具,它能够有效地确定哪些知识是冗余的,哪些知识是有用的,有效地减少训练时间,从而很好地弥补了人工智能的不足。因此,基于传统人工智能的机械故障诊断技术已开始转向以粗糙集理论为代表的计算智能领域。以某型农业机械故障诊断为例,选择运用粗糙集理论,通过对故障诊断原始数据的分析处理和属性约简后,得到了简明的故障诊断规则,取得了良好地故障诊断效果。Rough set theory is a tool for dealing with fuzzy and uncertain knowledge. It can point out effectively whatknowledge is redundant and what knowledge is useful, and it can reduce the training period effectively, thus to make up the deficiency of artificial intelligence. Therefore, the mechanical fault diagnosis based on traditional artificial intelligence has turned into the computational intelligence based on rough set theory. In this paper, taking an example of a type of ag- ricuhural mechanical fault diagnosis, it simplifies the fault diagnosis rules and achieves good effect to fault diagnosis with rough sets theory through the original data analysis and attribute reduction.

关 键 词：农业机械 故障诊断 粗糙集 属性约简 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]