基于SOFM网络的机械设备多类型信息融合与状态识别

MULTI-TYPE INFORMATION FUSION AND STATE IDENTIFICATION BASED SOFM

从信息源的角度来说 ,机械监测与诊断系统是一个多源信息处理系统。提出了利用信息压缩进行多类型特征信息融合的思想 ,研究了自组织特征映射 (SOFM)网络处理这种多类型信息融合的可视化状态识别方法。网络输出层激活结点的轨迹 ,可以正确直观地反映出多源信息所表示状态的潜在变化特征 ,从而便于识别早期故障的发生与变化趋势。通过对试验及现场数据的融合处理 ,说明了所提出方法的有效性。

From the viewpoint of information source, the system of monitoring and diagnosis for machinery is one of multi source information processing systems. Various features can be reduced to three types:numeric, linguistic and graphics. Through translating the non numeric symptom into numeric one, information of various types can be denoted by multi dimension vector. So, the idea of features fusion of various types is proposed through information compression, and the method of how self organizing feature mapping (SOFM) network deals with it is studied. With the trace of active nodes on output layer, the underlying features varying of state represented by multi source information can be observed correctly and visually, so occurrence and varying trend of faults can be identified early. The high performance of this method proposed is exempli fied by handling fusion in experiments and field work.