Abstract：The classification of cavitation in waterjet pump was researched by multi-sensor data fusion method based on singular value decomposition (SVD) and artificial neural networks (ANN). Time data fusion was used to fuse the acoustic and vibration signals based on SVD firstly, then the respective features were extracted, which were composed and used as entries of the ANN, the BP network and RBF network were applied to fuse the features for classification. The results show that the classification performance based on multi-sensor data fusion is better than single sensor, and the recognition rates are obviously improved after data fusing based on SVD, which is superior for weak feature detection and recognition of cavitation inception
苏永生;王永生;段向阳. 基于多传感器数据融合的喷水推进泵空化分类识别[J]. , 2012, 31(18): 93-95.
SU Yong-sheng;WANG Yong-sheng;DUAN Xiang-yang. Classification of Cavitation in Waterjet Pump Based on Multi-Sensor Data Fusion. , 2012, 31(18): 93-95.
2012, Vol. 31 
