基于信息融合的支撑座早期松动故障诊断

采用基于信号分析的无模型检测方案和信息融合技术,对支撑座早期松动故障进行检测诊断.针对支撑座松动的小波包变换特征和功率谱特征进行特征融合与决策融合,同时采用基于熵度量的无监督特征约简方法对功率谱特征进行约简,有效地减少了特征数目,加快了融合和诊断速度.特征融合与决策融合采用分层神经网络实现,该网络综合了局部融合和全局融合的优点,具有很高的故障确诊率和很好的抗噪性能,无噪声样本综合确诊率达94.3%,有噪声样本综合确诊率达88.6%.

Early Loosening Fault Diagnosis of Clamping Support Based on Information Fusion

A novel global Non-Destructive Evaluation(NDE) technique based on information fusion was proposed to diagnose early loosening fault of clamping support.It is a kind of non-modeled method.Two feature extraction methods are used to extract feature,which are wavelet packet(WP) transform and power spectrum density(PSD) (analysis) based on FFT.During the loosening fault diagnosis,two local decisions are made by using WP feature and PSD feature respectively.Then the two features are fused to make another local decision.After that the three local decisions are fused to make global decision.A hierarchical neural network structure is put forward to implement feature fusion and decision fusion.The network has advantanges of both local fusion and global fusion,also it has high correct diagnosis ratio and good antinoise performance.Using the information fusion network,the correct diagnosis ratios of exemplars with no noise and with random noise reach 94.3% and 88.6% respectively.