基于PCA和自联想神经网络的核环境冷挤压切割刀具状态监测  

Monitoring for cold extrusion cutting tools in nuclear environment based on PCA and auto associative neural network

作　　者：袁沛[1,2] 蒋君侠 马飞[2] 金杰峰 来建良 YUAN Pei;JIANG Junxia;MA Fei;JIN Jiefeng;LAI Jianiang(School of Mechanical Engineering,Zhejiang University,Hangzhou 310058,China;Hangzhou Jingye Intelligent Technology Co.Ltd,Hangzhou 310051,China)

机构地区：[1]浙江大学机械工程学院,浙江杭州310058 [2]杭州景业智能科技股份有限公司,浙江杭州310051

出　　处：《浙江大学学报(工学版)》2025年第3期606-615,共10页Journal of Zhejiang University:Engineering Science

基　　金：“尖兵领雁+X”研发攻关计划资助项目(2024C04056(CSJ)).

摘　　要：在高放射性环境中,传感器部署受限,传动链噪声干扰,冷挤压切割刀具一致性差.为此提出基于外置电机旋转轴与进给轴电机扭矩信号的时频域统计、主成分分析(PCA)与自联想神经网络(AANN)相结合的刀具状态监测模型.基于旋转电机及进给电机扭矩波形提取时域统计特征及小波包能量特征形成原始训练集,利用原始训练集初步训练AANN模型,使用PCA重构原始训练集用于优化AANN模型局部结构参数,形成PCA-AANN刀具状态监测模型.基于实际样机的切割试验采集扭矩数据,对提出的PCA-AANN和现有AANN模型进行分析对比,结果表明PCA的引入有助于提高AANN模型鲁棒性,能有效降低刀具工作状态误报率,实现放射性环境下刀具状态的准确监测.所提方法为放射性环境中类似长传动链设备的状态监测提供了借鉴.A tool condition monitoring model combined with time-frequency domain statistics,principal component analysis(PCA)and auto associative neural network(AANN)was proposed based on motor torque signals of external motor rotation shaft and feed shaft,aiming at problems such as limited deployment of sensors in high radiation environment,noise interference of transmission chain and poor consistency of cold extrusion cutting tools.Firstly,time domain statistical features and wavelet packet energy features were extracted to form the original training set based on the torque waveform of rotary motor and feed motor.Then,the original training set was used to train the AANN model.Finally,the PCA was used to reconstruct the original training set to optimize the local structural parameters of the AANN model,and a PCA-AANN tool condition monitoring model was formed.The proposed PCA-AANN model was compared with the existing AANN model based on the torque data collected from the cutting test of the actual prototype,and experimental results showed that the introduction of PCA improved the robustness of AANN model,reduced the false alarm rate of tool operating state,and realized the accurate monitoring of tool status under radioactive environment.The proposed method provided a reference for the condition monitoring of similar transmission equipment under radioactive environment.

关 键 词：放射性 刀具状态监测 时域统计 小波包分解 主成分分析 自联想神经网络 

分 类 号：TH164[机械工程—机械制造及自动化]