Dual reduced kernel extreme learning machine for aero-engine fault diagnosis

Abstract

In order to improve the sparsity of kernel-based extreme learning machine (KELM), this paper proposed a novel method named dual reduced kernel extreme learning machine (DR-KELM). The proposed algorithm incorporates traditional greedy forward learning algorithm into backward learning algorithm to gain more sparsity and enhance testing time further. Compared to original KELM, the proposed method produces satisfactory performance of pattern recognition with fewer nodes, and reduces diagnostic consuming time from the tests on benchmark dataset. The DR-KELM application to aero-engine fault diagnosis also demonstrates its superior performance with more sparse structure.