考虑工况变化的数控刀架运行状态异常检测方法  

An Anomaly detection method for numerical control turrets considering working conditions

作　　者：胡炜[1,2,3] 陈传海 郭劲言[1,2] 刘志峰 申桂香[1,2] 于春明 HU Wei;CHEN Chuan-hai;GUO Jin-yan;LIU Zhi-feng;SHEN Gui-xiang;YU Chun-ming(Key Laboratory of CNC Equipment Reliability,Ministry of Education,Jilin University,Changchun 130022,China;College of Mechanical and Aerospace Engineering,Jilin University,Changchun 130022,China;KTH Royal Institute of Technology,Stockholm 25175,Sweden;Shenyang Machine Tool Co.,Ltd.,Shenyang 110142,China)

机构地区：[1]吉林大学数控装备可靠性教育部重点实验室,长春130022 [2]吉林大学机械与航空航天工程学院,长春1300225 [3]瑞典皇家理工学院,瑞典斯德哥尔摩25175 [4]沈阳机床股份有限公司,沈阳110142

出　　处：《吉林大学学报（工学版）》2022年第2期329-337,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51975249);吉林省重点科技攻关项目(20190302017GX).

摘　　要：针对数控刀架故障数据少、难获取,且运行数据随工况变化致使故障诊断困难的问题,提出了一种基于非故障数据并考虑工况变化的运行状态异常检测方法。该方法通过多元高斯分布模型和考虑工况变化的偏差特征建模,确定刀架运行状态异常数据的评判依据。首先,通过统计分析找到刀架在不同工作过程中的关键工况与信号特征;其次,分别选择线性回归、信息增益判别、广义回归神经网络方法建立工况与信号特征的关系模型,得到观测信号特征与给定信号特征之间的偏差;最后,采用刀架正常状态时的数据训练模型。通过大量工况变化实验与异常模拟实验,并与传统多元高斯分布模型比较,得出本文所提模型能排除工况变化的影响,并能更好地识别异常状态。The difficulties of failure data collection and operation data changeability hinder the application of fault diagnosis methods to turrets.Hence,an anomaly detection method using non-failure data and considering the change of working conditions was proposed for detecting turrets’anomaly state during operation.The method studied the judgment principle of abnormal data through the multivariate Gaussian distribution(MGD)and the deviation characteristic associated with working conditions.First,the key working conditions and signal characteristics in different turret working processes were determined through statistical analysis.Second,some methods like linear regression,information gain,and generalized regression neural network were selected to model their relationships,respectively.Following that,the deviation of observation from the given signal characteristics is calculated.Finally,the operation data from turret normal state were used to train the model.Many experiments under different working conditions and abnormal simulation were conducted to verify that the proposed model can eliminate the influence of working conditions on abnormal judgment compared to the traditional MGD model.

关 键 词：异常检测 工况 数控刀架 多元高斯分布 

分 类 号：TG659[金属学及工艺—金属切削加工及机床]