Research on Prediction of Stability of Torque Sensor Based on Neural Network

MENG Chen; JIANG Jile; GUO Bin; WU Kun; WU Shi

doi:10.12338/j.issn.2096-9015.2021.0633

Volume 66 Issue 5

Jul. 2022

Turn off MathJax

Article Contents

Article Navigation > Metrology Science and Technology > 2022 > 66(5): 8-14, 68

MENG Chen, JIANG Jile, GUO Bin, WU Kun, WU Shi. Research on Prediction of Stability of Torque Sensor Based on Neural Network[J]. Metrology Science and Technology, 2022, 66(5): 8-14, 68. doi: 10.12338/j.issn.2096-9015.2021.0633

Citation:

MENG Chen, JIANG Jile, GUO Bin, WU Kun, WU Shi. Research on Prediction of Stability of Torque Sensor Based on Neural Network[J]. Metrology Science and Technology, 2022, 66(5): 8-14, 68. doi: 10.12338/j.issn.2096-9015.2021.0633

Citation:

PDF( 1857 KB)

Research on Prediction of Stability of Torque Sensor Based on Neural Network

doi: 10.12338/j.issn.2096-9015.2021.0633

National Institute of Metrology, Beijing 100029, China

Accepted Date: 2022-04-07

Available Online: 2022-06-02

Publish Date: 2022-07-11

Abstract

Abstract

In torque verification, the fluctuation of torque value over time is not a known common function, and it is vital to explore the trend of its change and fluctuation for torque verification and dissemination of value of quantity. To explore the fluctuation characteristic of the torque sensor, the influence of medium- and long-term stability on the change of torque value in torque measurement is investigated, and a neural network is used to train the detection data under given conditions to predict the torque value at other time points. The torque value data obtained under strictly controlled experimental conditions for the last 12 years were used for training and analysis, the accuracy and reliability of the model were demonstrated by cross-validation, the torque value of the next calibration cycle was predicted, and the probability that the torque value of the next cycle meets the long-term stability of 0.03 level was finally calculated to be greater than 71.8%.
- long-term stability,
- neural network,
- cross-validation,
- torque sensor

FullText(HTML)

References(19)

References

[1]	Anil K Jain, Jianchang Mao, K Moidin Mohiuddin. Artificial Neural Networks: A Tutorial[J]. Computer, 1996, 29(3): 31-44. doi: 10.1109/2.485891
[2]	Lippmann R P. An introduction to computing with neural nets[J]. IEEE Assp Magazine, 1988, 4(2): 4-22.
[3]	DE Rumelhart, Hinton G E, Williams R J. Learning Representations by Back Propagating Errors[J]. Nature, 1986, 323(6088): 533-536. doi: 10.1038/323533a0
[4]	全国力值硬度计量技术委员会. 标准扭矩仪: JJG 557-2011 [S]. 北京: 中国计量出版社, 2011.
[5]	ISO/TC. Metallic materials-Calibration of force-proving instruments used for the verification of uniaxial testing machines: ISO 376: 2011[S]. Geneva: International Standards Organization, 2011.
[6]	全国法制计量管理计量技术委员会. 测量不确定度评定与表示: JJF1059.1-2012[S]. 北京: 中国质检出版社, 2012.
[7]	Robert Hecht-Nielsen. Theory of the backpropagation neural network[C]. In Proceeding of the International Joint Conference on Neural Networks(IJCNN). 1989: 593-605.
[8]	Hornik K. Approximation capabilities of multilayer feedforward networks[J]. Neural Networks, 1991, 4(2): 251-257. doi: 10.1016/0893-6080(91)90009-T
[9]	Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal Approximators[J]. Neural Networks, 1989, 2: 359-366. doi: 10.1016/0893-6080(89)90020-8
[10]	Cybenko G. Approximation by superpositions of a sigmoid function[J]. Mathematics of Control, Signals, and Systems, 1989, 2: 303-314. doi: 10.1007/BF02551274
[11]	Baratto-Filho F, Freitas J, Gabardo M C L, et al. Analysis of reciprocating system real-time torque variation during root canal preparation[J]. BMC Research Notes, 2019, 12(1): 47. doi: 10.1186/s13104-019-4080-z
[12]	Kwak S W, Ha J H, Cheung S P, et al. Comparison of In Vitro Torque Generation during Instrumentation with Adaptive Versus Continuous Movement[J]. Journal of Endodontics, 2019(45): 803-807.
[13]	Kwak S W, Ha J H, Cheung S P, et al. Effect of the Glide Path Establishment on the Torque Generation to the Files during Instrumentation An InVitro Measurement[J]. J Endod, 2018, 44(3): 496-500. doi: 10.1016/j.joen.2017.09.016
[14]	Bürklein S, Stüber JP, Schäfer E. Real-time dynamic torque values and axial forces during preparation of straight root canals using three different endodontic motors and hand preparation[J]. Int Endod J, 2019(52): 94-104.
[15]	Arvind Yadav, Boggavarapu Bhanu Venkata Satya Vara Prasad, Ramesh Kumar Mojjada, et al. Application of Artificial Neural Network and Genetic Algorithm Based Artificial Neural Network Models for River Flow Prediction[J]. Revue d'Intelligence Artificielle, 2020, 34(6): 745-751. doi: 10.18280/ria.340608
[16]	蒋继乐, 吴鲲, 郭斌, 等. 应用于惯性导航系统中的扭矩计量技术研究[J]. 计量科学与技术, 2021(2): 59-62,75.
[17]	张智敏, 张跃, 孟峰, 等. 基于空气轴承支撑技术的高准确度大扭矩标准装置研究[J]. 计量学报, 2021, 42(3): 271-275. doi: 10.3969/j.issn.1000-1158.2021.03.02
[18]	靳浩元, 刘军. 测量不确定度的评定方法及应用研究[J]. 计量科学与技术, 2021, 65(5): 124-131.
[19]	陈凌峰. 标准不确定度A类评定中极差法的深入讨论[J]. 计量学报, 2019, 40(2): 347-352.