[4] [4] JIAO X X, JING B, HUANG Y F, et al.Research on fault diagnosis of airborne fuel pump based on EMD and probabilistic neural networks［J］.Microelectronics Reliability, 2017, 75:296-308.

[7] [7] YU J S, MO B H, TANG D Y, et al.Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter［J］.Quality Engineering, 2017, 29(3):536-546.

[9] [9] DURODOLA J F, LI N, RAMACHANDRA S, et al.A pattern recognition artificial neural network method for random fatigue loading life prediction［J］.International Journal of Fatigue, 2017, 99(6):55-67.

[10] [10] SEPASI S, GHPRBANI R, LIAW B Y.A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter［J］.Journal of Power Sources, 2014, 245(1):337-344.

[12] [12] KHELIF R, CHEBEL-MORELLO B, MALINOWSKI S, et al.Direct remaining useful life estimation based on support vector regression［J］.IEEE Transactions on Industrial Electronics, 2017, 64(3):2276-2285.

[13] [13] LIN C J.A comparison of methods for multiclass support vector machines［J］.IEEE Transactions on Neural Networks, 2002, 13(2):415-425.