Recursive reduced kernel based extreme learning machine for aero-engine fault pattern recognition

Kernel based extreme learning machine (K-ELM) has better generalization performance than basic ELM with less tuned parameters in most applications. However the original K-ELM is lack of sparseness, which makes the model scale grows linearly with sample size. This paper focuses on sparsity of K-ELM and proposes recursive reduced kernel based extreme learning machine (RR-KELM). The proposed algorithm chooses samples making more contribution to target function to constitute kernel dictionary meanwhile considering all the constraints generated by the whole training set. As a result it can simplify model structure and realize sparseness of K-ELM. Experimental results on benchmark datasets show that no matter for regression or classification problems, RR-KELM produces more compact model structure and higher real-time in comparison with other methods. The application of RR-KELM for aero-engine fault pattern recognition is also given in this paper. The simulation results demonstrate that RR-KELM has a high recognition rate on aero-engine fault pattern based on measurable parameters of aero-engine.