考虑工况变化的数控刀架运行状态异常检测方法

doi:10.13229/j.cnki.jdxbgxb20211079

Abstract

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

Wei HU,Chuan-hai CHEN,Jin-yan GUO,Zhi-feng LIU,Gui-xiang SHEN,Chun-ming YU. An Anomaly detection method for numerical control turrets considering working conditions[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 329-337.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Table 2

Fig.6

Table 3

Fig.7

References 13

1	杨兆军, 陈传海, 陈菲, 等. 数控机床可靠性技术的研究进展[J].机械工程学报, 2013, 49(20): 130-139.
	Yang Zhao-jun, Chen Chuan-hai, Chen Fei, et al. Progress in the research of reliability technology of machine tools[J]. Journal of Mechanical Engineering, 2013, 49(20): 130-139.
2	张英芝, 郑珊, 申桂香, 等. 采用重要度和模糊推理的数控刀架危害性分析[J]. 吉林大学学报: 工学版, 2012, 42(5): 1157-1162.
	Zhang Ying-zhi, Zheng Shan, Shen Gui-xiang, et al. Criticality analysis for CNC turret adopting importance and fuzzy reasoning[J]. Journal of Jilin University(Engineering and Technology Edition), 2012, 42(5): 1157-1162.
3	陈斌, 陈松灿, 潘志松, 等. 异常检测综述[J]. 山东大学学报: 工学版, 2009, 39(6): 13-23.
	Chen Bin, Chen Song-can, Pan Zhi-song, et al. Survey of outlier detection technologies[J]. Journal of Shandong University(Engineering Science), 2009, 39(6): 13-23.
4	周东华, 魏慕恒, 司小胜. 工业过程异常检测、寿命预测与维修决策的研究进展[J]. 自动化学报, 2013, 39(6): 711-722.
	Zhou Dong-hua, Wei Mu-heng, Si Xiao-sheng. A survey on anomaly detection, life prediction and maintenance decision for industrial processes[J]. Acta Automatica Sinica, 2013, 39(6): 711-722.
5	丁建伟. 数据驱动的复杂装备异常检测方法[D]. 北京: 清华大学计算机科学与技术学院, 2015.
	Ding Jian-wei. Data-driven complex equipment anomaly detection methods[D]. Beijing: College of Computer Science and Technology, Tsinghua University, 2015.
6	Ding Nan, Ma Hao-xuan, Gao Huan-bo, et al. Real-time anomaly detection based on long short-Term memory and Gaussian mixture model[J]. Computers & Electrical Engineering, 2019, 79: 106458.
7	Li G Y, Lee H, Rai A, et al. Analysis of operational and mechanical anomalies in scheduled commercial flights using a logarithmic multivariate Gaussian model[J]. Transportation Research, 2020, 110(1): 20-39.
8	Zeng X J, Yang M, Bo Y F. Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation[J]. International Journal of Electrical Power & Energy Systems, 2020, 123: 106233.
9	Zhao Hong-shan, Liu Hui-hai, Hu Wen-jing, et al. Anomaly detection and fault analysis of wind turbine components based on deep learning network[J]. Renewable Energy, 2018, 127(11): 825-834.
10	Sarmadi H, Karamodin A. A novel anomaly detection method based on adaptive mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects[J]. Mechanical Systems and Signal Processing, 2020, 140(1): 106495.
11	谢群亚. 基于故障树的数控刀架可靠性分配方法研究[D]. 长春: 吉林大学机械与航空航天工程学院, 2017.
	Xie Qun-ya. Research on reliability allocation method of CNC servo turret based fault tree analysis[D]. Changchun: School of Mechanical and Aerospace Engineering, Jilin University, 2017.
12	杨兆军, 杨川贵, 陈菲, 等. 基于最小二乘算法和SVDUKF算法的电液伺服加载优化[J]. 吉林大学学报: 工学版, 2014, 44(2): 392-397.
	Yang Zhao-jun, Yang Chuan-gui, Chen Fei, et al. Optimization of the electro-hydraulic servo loading based on least square and SVDUKF algorithms[J]. Journal of Jilin University(Engineering and Technology Edition), 2014, 44(2): 392-397.
13	胡炜. 面向数控刀架可靠性试验的CPS研究[D]. 长春: 吉林大学机械与航空航天工程学院, 2019.
	Hu Wei. Research on CPS of NC turret reliability test[D]. Changchun: School of Mechanical and Aerospace Engineering, Jilin University, 2019.

Related Articles 10

[1]	Shao-jiang DONG,Peng ZHU,Xue-wu PEI,Yang LI,Xiao-lin HU. Fault diagnosis of rolling bearing under variable operating conditions based on subdomain adaptation [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 288-295.
[2]	Yu-mei LIU,Ning-guo QIAO,Jiao-jiao ZHUANG,Peng-cheng LIU,Ting HU,Li-jun CHEN. Anomaly detection of rail vehicle gearbox based on multi⁃sensor data fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1465-1470.
[3]	YU He, QIN Gui-he, SUN Ming-hui, YAN Xin, WANG Xuan-zhe. Cyber security and anomaly detection method for in-vehicle CAN [J]. 吉林大学学报(工学版), 2016, 46(4): 1246-1253.
[4]	LUO Hai-tao, ZHOU Wei-jia, WANG Hong-guang, WU Jia-feng. Mechanical analysis of friction stir welding robot under typical working conditions [J]. 吉林大学学报(工学版), 2015, 45(3): 884-891.
[5]	WANG Yu-lei, ZHAO Chun-hui, QI Bin. Hyperspectral anomaly detection algorithm based on spectral similarity scale [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 148-153.
[6]	CHAI Zheng-yi, WU Hui-xin, WU-Yong. Optimization algorithm for immune real-value detector generation [J]. , 2012, 42(05): 1251-1256.
[7]	CHAI Zheng-yi, WANG Xian-rong, WANG Liang. Real-value negative selection algorithm for anomaly detection [J]. 吉林大学学报(工学版), 2012, 42(01): 176-181.
[8]	ZHANG Shu-pei,ZHANG You-kun,WANG Qing-nian,ZHANG Lei. CVT ratio control strategy optimization under non-steady working conditions [J]. 吉林大学学报(工学版), 2010, 40(04): 895-0900.
[9]	ZHANG Shu-pei,ZHANG You-kun,WANG Qing-nian,ZHANG Lei. CVT ratio control strategy optimization under non-steady working conditions [J]. 吉林大学学报(工学版), 2010, 40(04): 895-0900.
[10]	Zhang Jun；Zhang Guo-ying；Liu Yu-shu. Network anomaly detection based on attributes similarity and cloud model [J]. 吉林大学学报(工学版), 2006, 36(06): 954-0957.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

关系模型	F检验（p值）	t-检验（p值）		决策系数R
关系模型	F检验（p值）	系数1	系数2	决策系数R
油压-特征A_sk	0.00	0.00	0.00	0.764
油压-特征A_sj	0.00	0.00	0.02	0.858
油压-特征B_sk	0.00	0.00	0.00	0.961
油压-特征B_sj	0.00	0.00	0.00	0.848

关系模型	F检验（p值）	t-检验（p值）			决策系数R
关系模型	F检验（p值）	系数1	系数2	系数3	决策系数R
式（23）	0.00	0.00	0.00	-	0.985
式（24）	0.00	0.00	0.00	0.00	0.851

动作	信号特征	μ	S	ε
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

An Anomaly detection method for numerical control turrets considering working conditions

RICH HTML

PDF (PC)