Soft one-class extreme learning machine for turboshaft engine fault detection

Abstract

One-class extreme learning machine (OC-ELM) is a very common one-class classification algorithm. In the process of constructing the OC-ELM, a part of training samples will be removed by the algorithm, which leads to inconsistency between its constraint condition and decision function, and is also known as the problem of hard margin flaw. In order to solve this problem, a soft one-class extreme learning machine (SOC-ELM) is proposed by assigning an appropriate margin to each training sample. Experimental results on benchmark data sets show that SOC-ELM has a strong classification performance. When the SOC-ELM is used for fault detection of a turboshaft engine, it can achieve good detection effectiveness and has strong robustness.