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

• DOI: 10.1016/j.neucom.2016.06.069
• Publication Date: 2016-07-21T15:08:34Z
• Authors: Cheng-Xin You; Jin-Quan Huang; Feng Lu
• Keywords: Extreme Learning Machine; Kernel (algebra); Benchmark (surveying); Aero engine

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. A new algorithm is proposed in this paper to realize the sparsity of K-ELM.The algorithm makes better use of data information and builds more parsimonious models.The algorithm is used to recognize the aero-engine fault pattern and it works well.