A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise
A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise
复制标题
基于加权可拓神经网络的带噪声小样本汽轮发电机组故障诊断模型
DOI：
10.1016/j.cja.2020.06.024
复制
发表时间：
2020-10-01
影响因子：
5.7
通讯作者：
Sheng, Xin
中科院分区：
文献类型：
--
作者：
Wang, Tichun;Wang, Jiayun;Sheng, Xin
在数据驱动的汽轮发电机组故障诊断中，故障样本的获取成本较高，且不可避免地含有噪声，这都会导致诊断模型的识别性能不理想。针对这些问题，提出了一种基于加权扩展神经网络的汽轮发电机组故障诊断模型。小波神经网络是一种新型的神经网络，它具有三种连接权和一种改进的关联函数。利用扩展神经网络(ENN)、支持向量机(SVM)、相关向量机(RVM)和极限学习机(ELM)等模型对模型的性能进行了验证。结果表明，在含噪小样本集上，该模型具有较高的识别精度、较少的样本数和较强的抗噪能力。这一研究结果可作为小样本噪声下汽轮发电机组故障诊断的有力模型。(三)2020中国航空航天学会。爱思唯尔有限公司制作和主办。
In data-driven fault diagnosis for turbo-generator sets, the fault samples are usually expensive to obtain, and inevitably with noise, which will both lead to an unsatisfying identification performance of diagnosis models. To address these issues, this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network (W-ENN). WENN is a novel neural network which has three types of connection weights and an improved correlation function. The performance of the proposed model is validated against Extension Neural Network (ENN), Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Extreme Learning Machine (ELM) based models. The results indicate that, on noisy small sample sets, the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability. The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets. (C) 2020 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.