基于EMD和SVM的机载燃油泵故障诊断研究

针对机载燃油泵故障数据来源较少、诊断效率较低、维护费用较高、缺乏有效故障特征的问题，利用机载燃油转输系统实验平台收集的振动信号和压力信号，提出了一种基于经验模态分解(EMD)和支持向量机(SVM)的机载燃油泵故障诊断方法。首先，利用EMD提取振动信号不同频段的能量值作为特征参量，并结合压力信号均值构造故障特征向量；其次，分别采用遗传算法(GA)、粒子群优化算法(PSO)、樽海鞘群算法(SSA)、网格搜索算法(GS)对SVM的惩罚参数和径向基函数(RBF)参数进行优化，并对优化后的SVM诊断性能进行了评估；最后，分别采用SVM、极限学习机(ELM)、BP神经网络作为分类器，并对3种分类器的诊断性能进行了评估。结果表明:采用3种群智能优化算法的SVM故障诊断率均能达到100%，寻优过程中均未陷入局部最优解，且寻优时间相当，其中GA的训练时间最短，可以采用GA对SVM参数进行寻优；当采用GA_SVM作为故障分类器时，用时较短，且故障诊断率较高，可以选用GA_SVM分类模型实现机载燃油泵的高效故障诊断。 

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.