摘要
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
性能参数预测技术是航空发动机健康管理的核心研究内容,越来越多的机器学习算法被应用于该领域,正则化极限学习机(Regularized extreme learning machine,RELM)是其中之一。但RELM的正则化参数确定非常消耗计算资源,使其在数据量较大的航空发动机性能参数预测领域表现出不适应性。本文使用前向和后向分割(Forward and backward segmentation,FBS)算法提升RELM性能,并引入自适应步长确定方法和一种改进的求解机制获得新的机器学习算法。新算法在保持良好泛化的同时,对正则化参数不敏感,极大地节省了计算资源。在公开数据集上的实验结果证明了新算法的优异性能。最后将新算法应用于航空发动机性能参数的预测,取得了优异的预测性能。
