基于粗糙集理论和朴素贝叶斯分类算法的汽轮发电机振动故障诊断

DIAGNOSIS OF VIBRATION FAULT ON TURBO-GENERATOR SET BASED ON ROUGH SET THEORY AND NAIVE BAYESIAN CLASSIFICATION ALGORITHM

汽轮发电机组结构及振动的复杂性使其故障具有多层次性和随机性,以及故障信息不完整性等特点。对此,提出了一种基于粗糙集理论与朴素贝叶斯分类算法的汽轮发电机组振动故障诊断方法。通过粗糙集理论求取最小属性约简集,并在此基础上利用朴素贝叶斯分类算法诊断出故障概率最大的区,最后针对具体的故障设定值对该方法进行验证。实际算例结果表明,该方法能在故障信息不完整甚至丢失核心属性的情况下得到较好的诊断结果,提高了系统诊断的容错性。

The complexity of turbo- generator set ＇s structure and it＇s vibration make its fault to have multigradation and randomness,as well as the fault information being not complete. For this,a method to diagnose fault of turbo -generator set＇s vibration based on rough set theory and naive Bayesian classificatin algorithm has been put forward. The minmum attribute reduction set is sought by using the rough set,and then the bigger area in which the faults may occur has been diagnosed by using the naive Bayesian classification algorithm, and finally, directing against the concrete fault settings, the said method being validated. Results of practical calculation examples show that the the said method can get better diagnosis result when the fault information isn＇t complete, and even the kernel attribute being lost, enhancing the fault - tolerance capability of the diagnosis system.