Feature-level Fusion Fault Diagnosis Based on PCA
基于主成分分析的多源特征融合故障诊断方法

In most of current application,fault diagnosis based on single sensor show bad performance and is difficult to achieve satisfactory accuracy owning to noises. In this paper,a multi-sensor multi-feature fusion fault diagnosis method was proposed. The method firstly collects part operating conditions using multiple sensors installed on it, Then a fusion model based on principal component analysis was constructed to fuse all extracted features from the different sensors.Finally,fault diagnosis was carried out according to the fused results. Experiments and results show that new method can achieve perfect performance which is also better than that achieved by singlcscnsor.