Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

doi:10.1007/s40436-015-0107-4

Advances in Manufacturing ›› 2015, Vol. 3 ›› Issue (2): 97-104.doi: 10.1007/s40436-015-0107-4

• • 下一篇

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

Ke-Sheng Wang^1,2, Zhe Li¹, Jørgen Braaten¹, Quan Yu¹

1 Knowledge Discovery Laboratory, Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway;
2 Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, P. R. China

收稿日期:2015-03-24 修回日期:2015-04-15 出版日期:2015-06-25 发布日期:2015-05-14
通讯作者: Ke-Sheng Wang E-mail:kesheng.wang@ntnu.no

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

Ke-Sheng Wang^1,2, Zhe Li¹, Jørgen Braaten¹, Quan Yu¹

1 Knowledge Discovery Laboratory, Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway;
2 Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, P. R. China

Received:2015-03-24 Revised:2015-04-15 Online:2015-06-25 Published:2015-05-14
Contact: Ke-Sheng Wang E-mail:kesheng.wang@ntnu.no

摘要/Abstract

摘要： It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article reports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks (ANNs). The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.

关键词: Backlash error, Artificial neural network (ANN), Machine centers, Predictive maintenance

Abstract: It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article reports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks (ANNs). The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.

Key words: Backlash error, Artificial neural network (ANN), Machine centers, Predictive maintenance

Ke-Sheng Wang, Zhe Li, Jørgen Braaten, Quan Yu. Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs[J]. Advances in Manufacturing, 2015, 3(2): 97-104.

参考文献

1. Sander-Tavallaey S, Saarinen K (2009) Backlash identification in transmission unit. In: The 18th IEEE conference on control applications (CCA) & intelligent control, Saint Petersburg, Russia,8-10 July, 2009

2. Ding BY, Cazzolato BS, Grainger S et al (2015) Active preload control of a redundantly actuated Stewart platform for backlash prevention. Robot Comput Integr Manuf 32:11-24

3. Slamani M, Nubiola A, Bonev IA (2012) Modeling and assessment of the backlash error of an industrial robot. Robotica30:1167-1175

4. Yoon SM, Choi WH, Lee MC (2014) Backlash compensation by smooth backlash inverse for haptic master device using cableconduit. In: The 14th international conference on control, automation and systems (ICCAS), Kintex, Korea, 22-25 October,2014

5. Kolnik I, Agranovich G (2012) Backlash compensation for motion system with elastic transmission. In: IEEE 27th convention of electrical & electronics engineers in Israel (IEEEI), Eilat, Israel,14-17 November, 2012

6. Han KH, Koh GO, Sung JM et al (2011) Adaptive control approach for improving control systems with unknown backlash. In: The 11th international conference on control, automation and systems (ICCAS), Gyeonggi-do, Korea, 26-29 October, 2011

7. Ling Q, Yan Z, Shen HH et al (2014) Robust switching control strategy for a transmission system with unknown backlash. Math Probl Eng 2014:719384

8. European Standard (2001) Maintenance terminology. European Committee for Standardization 13306, Brussels

9. Wang KS (2014) Key techniques in intelligent predictive maintenance (IPdM): a framework of intelligent fault diagnosis and prognosis system (IFDaPS). Adv Mater Res 1039:490-505. doi:10.4028/www.scienfific.net/AMR.1039.490

10. Moya MCC (2004) The control of the setting up of a predictive maintenance programme using a system of indicators. Omega32(1):57-75

11. Garcia MC, Sanz-Bobi MA, del Pico J (2006) SIMAP: intelligent system for predictive maintenance application to the health condition monitoring of a wind turbine gearbox. Comput Ind57(6):552-568

12. Vachtsevanos G, Lewis FL, Roemer M et al (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken

13. Khan AW, Chen WY (2011) A methodology for systematic geometric error compensation in five-axis machine tools. Int J Adv Manuf Technol 53(5-8):615-628

14. Wightman EJ (1972) Instrumentation in process control. Butterworth- Heinemann, Oxford

15. Liu HL, Xue XN, Tan GY (2010) Backlash error measurement and compensation on the vertical machining center. Engineering2(6):403-407

16. Youssef HA, El-Hofy H (2008) Machining technology: machine tools and operations. Chemical Rubber Company Press, London

17. Braaten J (2015) Data mining for intelligent green monitoring of machine centers. Dissertation, Norweigian University of Science and Technology

18. Siddique A, Yadava GS, Singh B (2003) Applications of artificial intelligence techniques for induction machine stator fault diagnostics: review. In: Proceedings of the IEEE international symposium on diagnostics for electric machines, power electronics and drives. IEEE, New York, pp 29-34

19. Rao MA, Srinivas J (2003) Neural networks: algorithms and applications. Alpha Science International Ltd, Pangbourne

[1]	Harald Rødseth, Per Schjølberg, Andreas Marhaug. Deep digital maintenance[J]. Advances in Manufacturing, 2017, 5(4): 299-310.
[2]	Zhe Li, Yi Wang, Ke-Sheng Wang. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario[J]. Advances in Manufacturing, 2017, 5(4): 377-387.
[3]	Saroj Kumar Padhi, Ranjeet Kumar Sahu, S. S. Mahapatra, Harish Chandra Das, Anoop Kumar Sood, Brundaban Patro, A. K. Mondal. Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy[J]. Advances in Manufacturing, 2017, 5(3): 231-242.
[4]	Zhen-You Zhang Ke-Sheng Wang. Wind turbine fault detection based on SCADA data analysis using ANN[J]. Advances in Manufacturing, 2014, 2(1): 70-78.

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价