Multiscale Fuzzy Entropy and PSO-SVM Based Fault Diagnoses for Airborne Fuel Pumps

The fault diagnoses of the airborne fuel pumps are important to the safety and performance of flight tasks. Therefore, fault information extraction and the methods of fault diagnoses for airborne fuel pumps have become critical areas of study. In this paper, a fault state extraction method for airborne fuel pump is proposed by combining multiscale fuzzy entropy (FE) and particle swarm optimization support vector machine (PSO-SVM). The vibration signals of airborne fuel pumps are non-linear and non-stationary, which makes it difficult to extract fault features. Firstly, a coarse-graining process is applied to address vibration signals of an airborne fuel pump, and several coarse-grained sequences are obtained under different scales. Secondly, the FE is used to calculate the fault features, which contains the main fault information in the first few scales. Then, the feature vectors of fault are divided into training data and test data which used for the fault diagnoses model. The training data is used to train the PSO-SVM model, and the testing data is used to verify the effectiveness of the proposed method. Finally, the vibration signal of the airborne fuel pump on our designed experimental platform is collected, and the dataset is used to test the proposed method. Also, the experimental results show that the proposed method can successfully diagnose faults in airborne fuel pumps.