一种改进的l_(p)-RWMKE-ELM故障诊断模型

Research on an improved l_(p)-RWMKE-ELM fault diagnosis model

针对装备各类故障样本分布不平衡、现有算法故障诊断精度较低的问题,通过引入p范数约束多核极限学习机和基于AdaBoost的集成学习策略,定义了一种p范数约束下正则化加权多核集成极限学习机的故障诊断模型.首先,在p范数约束下,基于各类故障样本自身规模,分别进行了两种自适应的样本权重分配;其次,在每层分类器的优化中,将多核学习的多源数据融合能力和极限学习机运算高效的特点相结合,同时,将样本的权重W更新融入到多核极限学习机的优化进程;最后,通过Adaboost集成策略,自适应提升富含信息的样本在模型中的权重,从而显著提升故障诊断的精度.以6个UCI公共数据集以及1个实装案例为例,进行了故障诊断实验.结果表明,与核极限学习机、加权核极限学习机(使用W((1))和W(2)加权方式)以及多核极限学习机(在1范数和p范数约束下)相比,诊断精度有显著提升;范数约束形式对模型的诊断性能影响有限.

As the service time of military equipment increases,equipment failure data is continuously accumulated during events such as routine maintenance,training,and combat readiness exercises,and the data presented is often imbalanced to varying degrees and consists of small samples.In addition,due to fault tolerances of various electrical component parameters in the equipment and widespread nonlinearity and feedback loops of the circuit,it is often difficult to accurately express the fault mechanism using mathematical models.This poses new challenges for the fault diagnosis of equipment.To address the aforementioned problems,machine learning methods are widely used for fault diagnosis.The essence of such methods is that they transform a fault diagnosis problem into a pattern recognition problem.By learning the characteristic data of normal modes and various failure modes,a diagnosis model is constructed and,ultimately,a diagnosis strategy is formed.Aiming at the problems of the unbalanced distribution of various fault samples from equipment and low fault diagnosis accuracy of existing algorithms,in this paper,we define a regularized weighted multiple kernel ensemble under a p-norm constraint by introducing a p-norm constraint weighted multicore extreme learning machine and an ensemble learning strategy based on the AdaBoost fault diagnosis model of extreme learning machine.Under the p-norm constraint,the model performed two types of adaptive sample weight distribution based on the size of various fault samples;simultaneously,the model combines the multisource data fusion and extreme learning abilities of the multiple kernel learning machine with high efficiency.The weight of a sample,W,is integrated into the optimization objective function of the multiple kernel extreme learning machine.Through the Adaboost integration strategy,the information-rich sample in the model is adaptively improved.Thus,the weight of a sample significantly improves the accuracy of fault diagnosis.Taking 6 UCI public data sets and 1 actual installation case as examples,a fault diagnosis experiment was conducted.The results of the experiment show that the model constructed in this study has significantly improved diagnostic accuracy compared with other models such as kernel extreme learning machine,weighted kernel extreme learning machine(W(1)and W(2)weighting method),and weighted multiple kernel extreme learning machine under 1-norm constraint,and the model’s diagnostic performance impact is limited.