摘要
针对采集的控制棒驱动机构(CRDM)振动信号中存在非平稳、强噪声失真信号,提出一种基于评价函数和误差反向传播(BP)网络的CRDM滚轮状态评估方法。信号经半软阈值去噪、局部均值分解(LMD)提取特征向量,特征向量组成的样本集经BP网络进行状态识别,引入评价函数对状态识别结果进行评价,依据评价结果进行失真样本剔除,保留新形成的样本集进行状态识别。结果表明,基于评价函数和BP网络的CRDM滚轮状态评估方法能有效对滚轮缺陷状态进行识别,解决了控制棒驱动机构滚轮状态难以进行准确识别的问题。
In this paper, an evaluation method for CRDM roller state based on the evaluation function and error back propagation training(BP) network is proposed for the non-stationary and strong noise distortion signals in the control rod drive mechanism(CRDM) vibration signals. The signal is denoised by semi-soft threshold, the feature vectors are extracted by local mean decomposition(LMD), and the sample set composed of feature vectors is identified by BP network for state recognition. An evaluation function is introduced to evaluate the results of state recognition. The distorted samples are removed according to the evaluation results, and the new sample set is retained for state recognition. The results show that this method can effectively identify the defect state of the rollers and effectively solve the difficulty in accurate identification of the rollers state of the control rod drive mechanism.
作者
焦猛
蔡琦
张黎明
杨晓晨
张永发
Jiao Meng;Cai Qi;Zhang Liming;Yang Xiaochen;Zhang Yongfa(College of Nuclear Science and Technology,Naval University of Engineering,Wuhan,430033,China;Science and Technology on Reactor System Design Technology Laboratory,Nuclear Power Institute of China,Chengdu,610213,China)
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2021年第1期133-137,共5页
Nuclear Power Engineering
基金
中国核动力研究设计院核反应堆系统设计技术重点实验室基金资助项目(HT-LW-02-2014007)
湖北省自然科学基金(2019cfc889)。
关键词
控制棒驱动机构
BP网络
状态识别
评价函数
Control rod drive mechanism
BP network
State recognition
Evaluation function
