基于粗糙遗传算法在打捆机齿轮箱故障诊断中的应用*

自走式连续作业打捆机是一款实现不停机连续打捆作业的新型秸秆收集装备,其关键功能部件齿轮箱发生故障会严重影响正常打捆工作。针对齿轮箱故障的防控和监测,提出一种结合粗糙集和遗传算法的故障诊断方法。该方法使用时域频域分析得到的多项故障特征参数作为条件属性,故障类型作为决策属性,并利用自适应遗传算法得到决策规则表,实现无需先验信息的属性约简和故障诊断。在齿轮箱故障诊断试验中,分别对不同故障类型进行信号采集和诊断分析,结果显示:该方法在无先验信息的条件下将12项故障特征参量约简为3项,根据决策规则表进行故障诊断的准确率为100%,结果表明该方法能准确判断故障的发生和故障类型,对实现故障监测和防控具有重要意义。

Application of rough genetic algorithm in fault diagnosis of gearbox of bundling machine

The self-propelled continuous operation baler is a new type of straw collection equipment that realizes continuous baling operations. The failure of one of its key functional components will seriously affect the regular baling work. This paper proposed a fault diagnosis method combining rough sets and genetic algorithms to monitor, prevent, and control gearbox faults. This method used the multiple fault characteristic parameters obtained from time-domain and frequency-domain analysis as conditional attributes and the fault type as decision attribute to obtain a decision rule table using the genetic algorithm and realize the attribute reduction without prior information. In the gearbox fault diagnosis experiment, signal acquisition and diagnosis analysis were carried out on different fault types. The results showed that the method reduced the 12 fault characteristic parameters into 3 when no prior information was given. The accuracy of fault diagnosis according to the decision rule table was 100%. The results showed that this method could accurately determine the occurrence and type of faults, which was of great significance to fault monitoring, prevention, and control.