无线传感器网络多级融合的机械故障诊断方法

针对无线传感器网络(wireless sensor networks,简称WSNs)在机械故障诊断应用中大量振动信号不能实时传输的问题,提出基于无线传感器网络多级分层信息融合的机械故障诊断方法。采用簇树网络结构扩大网络监测覆盖范围,将WSNs信息融合分为数据级融合、特征级融合及决策级融合3个级别,终端节点对原始振动信息进行数据级融合以提取特征信息,簇头节点对特征信息进行特征级融合得到模式识别结果,网关节点对识别结果进行决策级融合以评估机械设备运行状态。实验表明,该方法能有效应用于机械故障诊断。

Mechanical Fault Diagnosis Method Based on Multi-level Fusion in Wireless Sensor Networks

Considering the inability of wireless sensor networks (WSNs) to meet the real-time transmission of a large number of vibration signals when applied to mechanical fault diagnosis, a mechanical fault diagnosis method based on multi-level hierarchical information fusion in WSNs is proposed. A cluster tree network structure is used to enlarge the coverage of network monitoring. The information fusion in WSNs is divided into three levels: data level fusion, feature level fusion and decision level fusion. Characteristic information of the raw vibration signals is extracted by terminal nodes for data level fusion. This information is then processed by cluster head nodes to obtain pattern recognition results for feature level fusion. The results are processed by the gateway to assess the mechanical equipment operational condition for decision level fusion. This experiment shows that this method can be applied well in mechanical fault diagnosis.