摘要
由于柴油机气缸缸盖振动信号具有复杂的时频特性,通过此类信号实现其气门故障的诊断较为困难。尤其在多种故障并发的条件下,故障确诊更为不易。为此,借助遗传算法提出一种基于统计规则的智能数据挖掘技术,对在不同气门状态下采集的大量柴油机气门缸盖振动信号进行知识挖掘,得到了进行多种气门故障确诊的有效诊断特征。试验表明,这一技术智能高效,结果准确无误,具有重要实践意义。
As complex features often present in both the time and frequency domains of the vibration signals collected from engines, through which it is hard to distinguish the working condition of their valves. Particularly when multiple numbers of valve states are needed to identify, the work will become more difficult to be done. In view of above reason, a smart data mining technique based on statistic rule is developed with the aid of genetic algorithm. After analyzing a large number of vibration signals collected from a diesel engine used for experiments, a few numbers of criteria that are really effective for diagnosing the valve states are found finally. Experimental results show that the proposed technique is actually intelligent and effective for solving the kind of present problem. It paves a new way accessing to the comprehensive utilization of 'data mountain' currently formed in production fields.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2004年第10期25-29,共5页
Journal of Mechanical Engineering
基金
国家自然科学基金(No.50205021)
陕西省自然科学基金(2002E226) 资助项目
关键词
数据挖掘
柴油机
故障诊断
小波变换
遗传算法
Data mining Diesel engine Fault diagnosis Wavelet transform Genetic algorithm
