摘要
在进行样本不平衡数据建模时,支持向量机(SVM)无法保护故障信号的特征信息,针对这一问题,提出了一种基于模糊支持张量训练机(FSTTM)的状态评估方法。首先,利用多源故障信号在FSTTM中,构建了张量样本,并在模型中引入了张量训练(TT)分解方法,以提取高阶张量样本中包含的特征信息;然后,利用基于TT核函数,建立了线性不可分下的预测模型,解决了非线性数据的分类问题;最后,在目标函数中设计了模糊因子,使模型对数目较少一类样本及数目较多一类样本的倾向均衡,实现了对样本不平衡数据的有效分类。研究结果表明:采用FSTTM对两种不同的滚动轴承数据进行故障诊断实验,其故障识别准确率均在97%以上,且F-score指标达到0.9800以上;相对于传统支持张量机,FSTTM利用高阶张量和模糊因子构造预测模型,可实现对原始信号状态信息的充分利用和样本不平衡数据的准确分类。
Aiming at the problem that support vector machine(SVM)cannot protect the characteristic information of fault signal when modeling of sample imbalanced data,a fuzzy support tensor train machine(FSTTM)method was proposed in this paper.Firstly,in FSTTM,tensor samples were constructed by using multi-source fault signals,and tensor train(TT)decomposition method was introduced into the model to extract the feature information contained in high-order tensor samples.Then,the prediction model based on TT kernel function was established,which can solve the classification problem of nonlinear data.Finally,a fuzzy factor was designed in the objective function,which can make the tendency equilibrium of the type of sample with less number and the type with more number of samples,and realize the effective classification of sample unbalanced data.Two different roller bearing data were used for experimental analysis of fault diagnosis.The results show that the fault identification accuracy of FSTTM is more than 97%and the Fscore index is more than 0.9800.Compared with the traditional support tensor machine,high-order tensor and fuzzy factor are used FSTTM to construct the prediction model,which can make full use of the original signal state information and accurately classify the sample unbalanced data.
作者
王劲锋
薛玉石
山春凤
WANG Jin-feng;XUE Yu-shi;SHAN Chun-feng(YangZhou Technician Branch of JiangSu Union Technical Institute,YangZhou 225000,China;State Machinery Precision Co.,Ltd.,Zhengzhou 450142,China;Luoyang Bearing Research Institute Co.,Ltd.,Luoyang 471039,China)
出处
《机电工程》
CAS
北大核心
2022年第10期1405-1411,共7页
Journal of Mechanical & Electrical Engineering
基金
国家重点研发计划资助项目(2018YFB2000502)
江苏省职业技术教育学会2021—2022年度江苏职业教育研究立项课题(XHYBLX2021203)
江苏省教育科学研究院2020年江苏省现代教育技术研究课题(2019-R-82143)。
关键词
故障信号特征信息
模糊支持张量训练机
张量训练分解方法
支持向量机
样本不平衡数据建模
多源故障信号
模型分类性能
characteristic information of fault signal
fuzzy support tensor train machine(FSTTM)
tensor train(TT)decomposition method
support vector machine(SVM)
sample imbalance modeling
multi-source fault signals
model classification performance