Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 329-337.doi: 10.13229/j.cnki.jdxbgxb20211079

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An Anomaly detection method for numerical control turrets considering working conditions

Wei HU1,2,3(),Chuan-hai CHEN1,2,Jin-yan GUO1,2(),Zhi-feng LIU1,2,Gui-xiang SHEN1,2,Chun-ming YU4   

  1. 1.Key Laboratory of CNC Equipment Reliability，Ministry of Education，Jilin University，Changchun 130022，China
    2.College of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
    3.KTH Royal Institute of Technology，Stockholm 25175，Sweden
    4.Shenyang Machine Tool Co. ，Ltd. ，Shenyang 110142，China
  • Received:2021-10-22 Online:2022-02-01 Published:2022-02-17
  • Contact: Jin-yan GUO E-mail:weihu19@mails.jlu.edu.cn;gjy_jlu@163.com

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Abstract:

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.

Key words: anomaly detection, working condition, numerical control turret, multivariate Gaussian distribution

CLC Number: 

  • TG659

Fig.1

Working principle of traditional MGD"

Fig.2

Relationship model of signal features and working conditions in process of cutting action"

Fig.3

Experimental diagram"

Table 1

Goodness of fit of relationship model of working conditions and features in process of loosening and locking actions"

关系模型

F检验

（p值）

t-检验（p值）决策 系数R
系数1系数2
油压-特征Ask0.000.000.000.764
油压-特征Asj0.000.000.020.858
油压-特征Bsk0.000.000.000.961
油压-特征Bsj0.000.000.000.848

Fig.4

Estimated and observed signal features in process of loosening and locking actions"

Fig.5

Estimated and observed signal features in process of tool-changing actions"

Table 2

Goodness of fit of relationship model of working conditions and features in process of tool-changing actions"

关系模型F检验 （p值）t-检验（p值）决策系数R
系数1系数2系数3
式（23）0.000.000.00-0.985
式（24）0.000.000.000.000.851

Fig.6

Estimated and observed signal features in process of cutting actions"

Table 3

Parameter results of proposed model"

动作信号特征μSε
松开A，B［ 5.4×10-11 7.1×10-11］［8.5×10-5 -1.3×10-4 ； -1.3×10-4 1.7×10-3］0.4460
锁紧A，B［ 1.3×10-10 -2.0×10-17］［3.7×10-3 -2.2×10-4 ； -2.2×10-4 3.4×10-3］0.0766
转位D，E［ 1.4×10-10 -1.1×10-17］［5.4×10-4 -9.3×10-3 ； -9.3×10-3 23.64］0.4725
切削G，H［-7.4×10-5 0.4774］［7.8×10-5 2.7×10-5 ； 2.7×10-5 1.3036］0.0003

Fig.7

Abnormal detection results"

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