摘要
粗糙集理论能够有效地处理不精确、不完整的数据和知识,并从中发现隐含知识,提示潜在规律。提出一种基于粗糙集理论的大型旋转机械故障诊断和知识获取模型。该模型从包含冗余和不一致信息的原始数据出发, 构建决策表,通过属性约简和基于分明矩阵的属性值约简获取故障诊断的最小约简属性集和诊断规则,并建立诊断规则知识库。基于该模型以某旋转注水机组故障分析为例,从来自实际的经验数据获取旋转注水机组转子故障诊断的规则知识,其属性约简率可达25％,并能有效解决旋转注水机组故障诊断中规则获取的知识冗余或缺失问题,验证了其有效性。
Rough set method is good for dealing with the knowledge under the condition of imprecise and inconsistent information to acquire hidden knowledge and principle. A model of the fault diagnosis and decision rules acquisition for large rotating machinery based on Rough set is put forward. According to the inconsistent and redundant original information, the decision table is formed and the fault attributes are reduced based on Rough set theory. The attributes＇ value is reduced by Showron discernibility matrix. Finally the minimum reduced attributes set and diagnosis rules database are established. This model is used for a rotating water injection machinery rules acquisition in practice. The original rough information is reduced to get the fault rules with 25% attributes＇ reducing rate for the rotating water injection machinery. Its validity is proved.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2006年第B05期135-138,共4页
Journal of Mechanical Engineering
基金
国家自然科学基金(50375017)北京市自然科学基金(3042006)北京市重点实验室基金(030314)资助项目。
关键词
旋转注水机组
故障诊断
规则获取
粗糙集
Rotating water injection machinery set Fault diagnosis Rules acquisition Rough set
