IJAT Vol.15 No.4 pp. 547-552
doi: 10.20965/ijat.2021.p0547


Electrical System Design and Fault Analysis of Machine Tool Based on Automatic Control

Yiping Yang, Hongyan Wu, and Jianmin Ma

Xuchang Vocational Technical College
4336 Xinxing Road, Xuchang City, Henan 461000, China

Corresponding author

November 2, 2020
March 19, 2021
July 5, 2021
automatic control, back-propagation neural network, electrical system, fault analysis, machine tool

Automatically controlled machine tools have been used extensively in the industrial field, and fault analysis methods have garnered increasing attention. This paper first describes the software and hardware design of a machine tool and then presents a fault analysis of the machine tool. The fault types of machine tools are analyzed. A signal is obtained from a vibration sensor, the characteristic value is extracted, and the fault is analyzed using a back-propagation neural network (BPNN). The experimental results show that the BPNN yields the best performance when the structure is 8-9-8, and its recognition rate is 97.22% for different types of faults. Meanwhile, the recognition rate of naive Bayes is only 76.73%, and that of a support vector machine is only 85.55%, which is significantly lower than that of the BPNN. The results show that the BPNN is effective in fault analysis and can be promoted and applied more extensively.

Cite this article as:
Y. Yang, H. Wu, and J. Ma, “Electrical System Design and Fault Analysis of Machine Tool Based on Automatic Control,” Int. J. Automation Technol., Vol.15 No.4, pp. 547-552, 2021.
Data files:
  1. [1] T. Huang, J. Yan, M. Jiang, W. Peng, and H. Huang, “Reliability analysis of electrical system of computer numerical control machine tool based on Bayesian networks,” J. Shanghai Jiaotong Univ. (Sci.), Vol.21, pp. 635-640, 2016.
  2. [2] Q. Wang, Z. Huo, and G. Lee, “The Two-Axis Linkage System Design of CNC Machine Based on PLC,” ITM Web Conf., Vol.26, 03006, 2019.
  3. [3] Y. Zhang, L. Mu, G. Shen, Y. Yu, and C. Han, “Fault diagnosis strategy of CNC machine tools based on cascading failure,” J. Intell. Manuf., Vol.30, pp. 2193-2202, 2019.
  4. [4] G. Shen, C. Han, B. Chen, and L. Dong, “Fault Analysis of Machine Tools Based on Grey Relational Analysis and Main Factor Analysis,” J. Phys. Conf. Ser., Vol.1069, 012112, 2018.
  5. [5] G. Zhang, Y. Ran, and Y. Chen, “Risk Analysis of Coupling Fault Propagation Based on Meta-Action for Computerized Numerical Control (CNC) Machine Tool,” Complexity, Vol.2019, pp. 1-11, 2019.
  6. [6] S. H. H. Zargarbashi and J. Angeles, “Identification of error sources in a five-axis machine tool using FFT analysis,” Int. J. Adv. Manuf. Tech., Vol.76, pp. 1353-1363, 2015.
  7. [7] B. Luo, H. Wang, H. Liu, B. Li, and F. Peng, “Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification,” IEEE T. Ind. Electron., Vol.66, pp. 509-518, 2019.
  8. [8] J. Herwan, S. Kano, O. Ryabov, H. Sawada, and M. Watanabe, “Comparing Vibration Sensor Positions in CNC Turning for a Feasible Application in Smart Manufacturing System,” Int. J. Automation Technol., Vol.12, No.3, pp. 282-289, 2018.
  9. [9] L. Liu, “Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network,” IEEE Access, Vol.7, pp. 47794-47803, 2019.
  10. [10] W. Zhang, G. Han, J. Wang, and Y. Liu, “A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction,” IEEE Access, Vol.7, pp. 11736-11746, 2019.
  11. [11] Q. H. Li and D. Liu, “Aluminum Plate Surface Defects Classification Based on the BP Neural Network,” Appl. Mech. Mater., Vol.734, pp. 543-547, 2015.
  12. [12] A. O. A. Salam, “A Generalized Architecture for Academic and Industrial Automation and Control via Internet,” Int. J. Automation Technol., Vol.8, No.4, pp. 598-610, 2014.
  13. [13] Q. Zhou, P. Yan, H. Liu, Y. Xin, and Y. Chen, “Research on a configurable method for fault diagnosis knowledge of machine tools and its application,” Int. J. Adv. Manuf. Tech., Vol.95, pp. 937-960, 2018.
  14. [14] Y. Guo, Y. Sun, and K. Wu, “Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing,” Int. J. Adv. Robot. Syst., Vol.17, 172988141989801, 2020.
  15. [15] J. Yan, H. S. Yin, J. Zhou, Y. F. Li, and H. Z. Huang, “Reliability Analysis of Electrical System of CNC Machine Tool Based on Dynamic Fault Tree Analysis Method,” J. Donghua Univ., Vol.32, pp. 1042-1046, 2015.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024