Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

作者：Yang, Xinyi^*; Pang, Shan; Shen, Wei; Lin, Xuesen; Jiang, Keyi; Wang, Yonghua

来源：International Journal of Aerospace Engineering, 2016, 2016: 7892875.

DOI：10.1155/2016/7892875

• DEFECT DIAGNOSTICS
• ALGORITHM

A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.

• 出版日期2016
• 单位鲁东大学; 中国人民解放军海军航空工程学院