基于POS-RELM的发动机复合故障在线诊断研究
Information Technology & Informatization(2016)
Abstract
本文针对发动机复合故障(失火故障、进气门间隙故障和排气门间隙故障)诊断中实时、高效的要求,将并行及正则算法引入在线贯序极限学习机(Online Sequential Extreme Learning Machine, OS-ELM),提出了一种可实现复合故障诊断的并行在线正则极限学习机(Parallel Online Sequential Regularized Extreme Learning Machine, POS-RELM)。通过试验数据可以得出,基于整周期分段特征提取法的POS-RELM算法相比于传统的神经网络分类算法更适合于发动机故障的在线监测及故障诊断。。
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