FAULT DIAGNOSIS METHOD OF AIRBORNE FUEL PUMP BASED ON CEEMD SHANNON ENTROPY AND GAPSO-SVM - BAO Jie; JING Bo; JIAO XiaoXuan; ZHANG QingYi; ZHANG Yu (Jixie qiangdu)
The airborne fuel pump is a key component of the fuel system. In view of the phenomena of mode aliasing and excessive residual component in the process of signal decomposition and reconstruction, a fault diagnosis method for airborne fuel pump based on CEEMD Shannon entropy and improved SVM is proposed. The signals of shell vibration and outlet pressure under various working conditions are obtained on the fault diagnosis test bench of airborne fuel pump. Then in the simulation I decomposed the vibration signals by using CEEMD method and calculated the Shannon entropy of IMF. Based on the above results, I selected the energy value and the mean value of pressure signal as the input eigenvectors of SVM, and used the SVM optimized by Gapso the to diagnose the fault types of fuel pump. Compared with BP neural network, SVM optimized by particle swarm optimization（PSO） and SVM optimized by genetic algorithm（GA）, the results showed that the model of SVM diagnosis optimized by GA has the advantages of fast training, high accuracy and short time-effect, and it has good engineering application value.