Fault diagnosis of airborne fuel pump based on EMD and SVM

For the problems of less onboard fuel pump fault data source, low diagnosis efficiency, high maintenance costs, and lack of effective fault characteristics, we use vibration signals and pressure signals collected from onboard fuel transfer system experimental platform, and put forward an onboard fuel pump fault diagnosis method based on Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM). First, EMD is used to extract values of vibration signals energy as characteristic parameters at different frequency bands, and fault characteristic vectors are constructed by combining with the mean value of port pressure signals. Then, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA) and Grid Search (GS) algorithm are used to optimize the penalty parameters c and Radial Basis Function (RBF) parameters g of SVM, and the optimized SVM diagnostic performance is evaluated. Finally, SVM, Extreme Learning Machine (ELM) and BP neural network are used as classifiers, and the diagnostic performance of the three classifiers is evaluated. The results show that the fault diagnosis rates of the SVM using the three-population intelligent optimization algorithm can reach 100%, none of them fall into the local optimal solution during the optimization process, and the optimization time is equal. Among them, the training time of GA is the shortest, so GA can be used to optimize the SVM parameters. When GA_SVM is used as the fault classifier, the time is shorter and the fault diagnosis rate is higher. Therefore, the GA_SVM classification model can be used to realize the efficient fault diagnosis of airborne fuel pump.