Chatter Monitoring Method of Rolling Mill Based on Dynamic Bayesian Network and Dempster/Shafer Evidence Theory

SHI Pei-ming; LIU Ao-yun; ZHANG Yi-lun; GAO Hao

doi:10.3969/j.issn.1000-1158.2022.09.12

PDF(3038 KB)

Acta Metrologica Sinica ›› 2022, Vol. 43 ›› Issue (9) : 1178-1185. DOI: 10.3969/j.issn.1000-1158.2022.09.12

Chatter Monitoring Method of Rolling Mill Based on Dynamic Bayesian Network and Dempster/Shafer Evidence Theory

SHI Pei-ming,LIU Ao-yun,ZHANG Yi-lun,GAO Hao

Author information +

History +

Abstract

A real-time monitoring method of rolling mill chatter based on dynamic bayesian network and DS (Dempster/Shafer) evidence theory is proposed. In the mentioned method, multiple characteristic parameters in time domain and frequency domain are preselected to represent the different characteristics of rolling mill vibration signals under different working conditions, and the symptom parameters with high sensitivity are selected by using the stability discrimination rate method. Then, the dynamic bayesian network and DS evidence theory real-time monitoring model is used to establish the real-time monitoring system of rolling mill chatter state, and the continuous speed load time slices are constructed. Three continuous speed load time slices are taken as the evidence body of DS evidence theory. At the same time, the trust method of optimizing the basic probability distribution is proposed to solve the conflict between the evidence bodies of DS evidence theory. Finally, the experiment is carried out on the rolling mill experimental platform, and the diagnosis results show that the recognition rate of different states of rolling mill chatter can reach 99.05%.

Key words

metrology / chatter monitoring / dynamic bayesian network / Dempster/Shafer evidence theory / rolling mill

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

SHI Pei-ming,LIU Ao-yun,ZHANG Yi-lun,GAO Hao. Chatter Monitoring Method of Rolling Mill Based on Dynamic Bayesian Network and Dempster/Shafer Evidence Theory[J]. Acta Metrologica Sinica. 2022, 43(9): 1178-1185 https://doi.org/10.3969/j.issn.1000-1158.2022.09.12

References

［1］郜志英, 臧勇, 曾令强. 轧机颤振建模及理论研究进展［J］. 机械工程学报, 2015, 51(16): 87-112.
Gao Z Y, Zang Y, Zeng L Q. Review of modelling and theoretical studies on chatter in the rolling mills［J］. Journal of Mechanical Engineering, 2015, 51(16): 87-112.
［2］Yang X, Tong C N, Yue G F. et al. Coupling Dynamic Model of Chatter for Cold Rolling［J］. Journal of Iron and Steel Research, International, 2010, 17( 12): 30-34.
［3］Mischke J. Equations of strip equilibrium during asymmetrical flat rolling［J］. Journal of Materials Processing Technology, 1996, 61(4): 382-394.
［4］刘彬, 王营辉, 姜佳磊, 等. 轧机主传动系统扭振减振器参数设计及特性分析［J］. 计量学报, 2020, 41(11): 81-87.
Liu B, Wang Y H, Jiang J L, et al. Parameter design and characteristic analysis of torsional vibration damper for main drive system of rolling mill［J］. Acta Metrologica Sinica, 2020, 41(11): 81-87.
［5］Xu Y, Li Q, Tong C N, et al. Vertical Vibration Model for Unsteady Lubrication in Rolls-Strip Interface of Cold Rolling Mills［J］. Advances in Mechanical Engineering, 2012, 2012: 1432-1437.
［6］Yun I S, Wilson W R D, Ehmann K F. Chatter in the strip rolling process, Part 1: Dynamic model of rolling［J］. Journal of Manufacturing ence and Engineering, 1998, 120(2): 330-336.
［7］Ehmann H. A dynamic model of the rolling process. Part I: homogeneous model［J］. International Journal of Machine Tools and Manufacture, 2000, 40(1): 1-19.
［8］刘彬, 姜佳磊, 潘贵翔, 等. 液压缸非线性弹簧力约束下轧机吸振器参数的优化［J］. 计量学报, 2019, 40(3): 378-383.
Liu B, Jiang J L, Pan G X, et al. Optimization of parameters of vibration absorber rolling mill system under the nonlinear spring force of hydraulic cylinder［J］. Acta Metrologica Sinica, 2019, 40(3): 378-383.
［9］侯东晓, 刘浩然, 李纪召, 等. 轧件不对称滞后变形下动态轧制力建模及仿真研究［J］. 机械工程学报, 2012, 48(014): 38-43.
Hou D X, Liu H R, Li J Z, et al. Modeling and simulation of dynamically rolling force under the asymmetric hysteretic deformation of workpiece［J］. Journal of Mechanical Engineering, 2012, 48(14): 38-43.
［10］时培明, 夏克伟, 刘彬, 等. 含间隙多自由度轧机传动系统非线性扭振动力特性［J］. 机械工程学报, 2012, 48(17): 57-64.
Shi P M, Xia K W, Liu B, et al. Dynamics behaviors of rolling mill’s nonlinear torsional vibration of multi-degree-of-freedom main drive system with clearance［J］. Journal of Mechanical Engineering, 2012, 48(17): 57-64.
［11］王松军. 1780热连轧机颤振分析及抑制措施［J］. 设备管理与维修, 2015(9): 80-82.
［12］刘彬, 蒋金水, 赵武, 等. 轧机转速波动测量的扭振监测实验研究［J］. 计量学报, 2007,28(3): 82-85.
Liu B, Jiang J S, Zhao W, et al. The speed oscillation measurement of the torsional vibration monitoring of the rolling mill［J］. Acta Metrologica Sinica, 2007,28(3): 82-85.
［13］Cui L, Huang J, Zhai H, et al. Research on the meshing stiffness and vibration response of fault gears under an angle-changing crack based on the universal equation of gear profile［J］. Mechanism and Machine Theory, 2016, 105: 554-567.
［14］Cui L L, Wang X, Wang H Q, et al. Improved Fault Size Estimation Method for Rolling Element Bearings Based on Concatenation Dictionary［J］. IEEE Access, 2019, 7: 22710-22718.
［15］Stief A, Ottewill J R, Baranowski J, et al. A PCA and Two-Stage Bayesian Sensor Fusion Approach for Diagnosing Electrical and Mechanical Faults in Induction Motors［J］. Industrial Electronics, IEEE Transactions on, 2019, 66(12): 9510-9520.
［16］Li K, Ping X, Wang H, et al. Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis［J］. Sensors (Basel, Switzerland), 2013, 13(6): 8013-8041.
［17］Ke L, Chen P, Wang S. An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network［J］. Sensors (Basel, Switzerland), 2012, 12(5): 5919-5939.
［18］Zhao Y F, Tong J J, Zhang L G, et al. Diagnosis of operational failures and on-demand failures in nuclear power plants: An approach based on dynamic Bayesian networks-ScienceDirect［J］. Annals of Nuclear Energy, 2020,138:107181.
［19］Gorga S, Otsuka K. Conversation scene analysis based on dynamic Bayesian network and image-based gaze detection［C］//Proceedings of the 12th International Conference on Multimodal Interfaces/7. International Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI 2010. Beijing, China, 2010.
［20］刘彬, 姜甲浩, 刘飞, 等. 基于轧件水平振动的轧机辊系振动补偿模型［J］. 计量学报, 2018, 39(1): 56-60.
Liu B, Jiang J H, Liu F, et al. Compensative vibration model of roll system based on horizontal vibration of rolled piece［J］. Acta Metrologica Sinica, 2018, 39(1): 56-60.