基于粗糙集和支持向量机的汽轮机组故障诊断

提出一种应用粗糙集(RS)和支持向量机(SVM)对汽轮发电机组故障诊断的模型。将采集到的振动信号进行傅立叶变换得到频谱特征,然后使用粗糙集进行知识约简去除冗余属性,得到决策表,将决策表作为支持向量机分类器的训练样本。通过学习,使构建的SVM机器能反映属性特征和故障类型的映射关系以达到故障诊断的目的。测试结果表明,应用粗糙集约简和SVM机器学习是一种有效的诊断方法,它能使诊断速度加快,而且诊断结果简单有效,有推广应用的价值。

Vibration fault diagnosis of steam turbine generating unit based on rough sets and support vector machine

A model of the vibration fault diagnosis for steam turbine generating unit was investigated by the method of combining rough sets(RS) theory and support vector machine(SVM).The vibration time-domain singal was transformed into frequency domain by fractional Fourier transform.RS was used to reduce redundant attributes,then a key decision table was obtained.The key table was acted as a learning sample to train SVM classifier.After training,SVM classifier can map the relationship between the attribute character and fault style,thus the objective of fault diagnosis was realized.The simulation experimental results show that the method of combining RS and SVM is efficient,which can lessen fault diagnosis time consumption.