An Intelligent Machine Condition Monitoring Model for Servo Systems

Year 2022, Volume: 10 Issue: 1, 23 - 29, 30.01.2022

Hayri Mutlu , Caner Akuner , Gazi Akgün

https://doi.org/10.17694/bajece.1018947

Abstract

The installation of industrial servo systems and the determination of control parameters are limited to the skills and knowledge of the commissioner. In addition, commissioned systems are often not re-optimized if environmental influences or loads change. The goal of this research is to create an artificial neural network (ANN) model for servo systems that will keep the servo system's proportional, integral, and derivative (PID) parameters working optimally. For this process, a machine condition monitoring algorithm developed with the ANN technique, which uses the data such as actual current, torque, power, position to be obtained from the servo system on an industrial controller, for the control and rearrangement of the parameters.

Keywords

Servo System, Artificial Neural Network, PLC, ProfiNET

References

• [1] «Jaen-Cuellar, A. Y., de J. Romero-Troncoso, R., Morales-Velazquez, L., & Osornio-Rios, R. A. (2013). PID-controller tuning optimization with genetic algorithms in servo systems. International Journal of Advanced Robotic Systems, 10(9), 324.».
• [2] «Chen, C. W., Chang, L. K., Liao, Y. T., Chung, C. H., Su, W. C., Chen, K. S., & Tsai, M. C. (2020, November). Tuning of Servo Drive Controller Based on Boosted Tree Model and Particle Swarm Optimization. In 2020 23rd International Conference on Electrical».
• [3] «Lin, Q. S., Yao, Y. F., & Wang, J. X. (2010, November). Simulation and application of neural network PID auto-tuning controller in servo-system. In 2010 2nd International Workshop on Database Technology and Applications (pp. 1-4). IEEE.».
• [4] Firoozian, R. (2014). Servo motors and industrial control theory. Springer..
• [5] «Jingjing, X., & Jiaoyu, L. (2013, May). Neural network PID controller auto-tuning design and application. In 2013 25th Chinese Control and Decision Conference (CCDC) (pp. 1370-1375). IEEE.».
• [6] «Aftab, M. S., & Shafiq, M. (2015, February). Adaptive PID controller based on Lyapunov function neural network for time delay temperature control. In 2015 IEEE 8th GCC Conference & Exhibition (pp. 1-6). IEEE.».
• [7] «Kumar, S., Mukherjee, D., Guchhait, P. K., Banerjee, R., Srivastava, A. K., Vishwakarma, D. N., & Saket, R. K. (2019). A comprehensive review of condition based prognostic maintenance (CBPM) for induction motor. Ieee Access, 7, 90690-90704.».
• [8] «Samhouri, M., Al-Ghandoor, A., Ali, S. A., Hinti, I., & Massad, W. (2009). An intelligent machine condition monitoring system using time-based analysis: neuro-fuzzy versus neural network. Jordan Journal of Mechanical and Industrial Engineering, 3(4), 294-».
• [9] Watson, G. A. (Ed.). (2006). Numerical analysis: proceedings of the Biennial Conference held at Dundee, June 28-July 1, 1977 (Vol. 630). Springer.
• [10] «Duymazlar, O., Engin, M., & Engin, D. (2020, June). Embedded Artificial Neural Network on PLCs to Predict Nonlinear System Responses. In 2020 9th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-4). IEEE.».
• [11] «Jung, I. S., Mulman, B. M., Thapa, D., Koo, L. J., Bae, J. H., Hong, S. H., ... & Wang, G. N. (2008, October). PLC control logic error monitoring and prediction using Neural Network. In 2008 Fourth International Conference on Natural Computation (Vol. 2,».
• [12] «Canedo, A., Ludwig, H., & Al Faruque, M. A. (2014). High communication throughput and low scan cycle time with multi/many-core programmable logic controllers. IEEE Embedded Systems Letters, 6(2), 21-24.».
• [13] «Li, J., & Gómez-Espinosa, A. (2018, November). Improving PID control based on neural network. In 2018 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) (pp. 186-191). IEEE.».
• [14] «Dias, A. L., Sestito, G. S., & Brandao, D. (2017). Performance analysis of profibus dp and profinet in a motion control application. Journal of Control, Automation and Electrical Systems, 28(1), 86-93.».
• [15] «Fontanelli, D., Macii, D., Rinaldi, S., Ferrari, P., & Flammini, A. (2013, May). Performance analysis of a clock state estimator for PROFINET IO IRT synchronization. In 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)».
• [16] «Wu, X., Xie, L., & Lim, F. (2014, October). Network delay analysis of EtherCAT and PROFINET IRT protocols. In IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society (pp. 2597-2603). IEEE.».
• [17] «Liu, L., Shan, L., Yan, J., Liu, C., & Dai, Y. (2018, June). An improved BFO algorithm for optimising the PID parameters of servo system. In 2018 Chinese Control And Decision Conference (CCDC) (pp. 3831-3836). IEEE.».
• [18] «Alan, M. (2020) Biosignal classification and disease prediction with deep learning. Master Thesis, Marmara Universities institute for graduate studies in pure and applied sciences, Istanbul, Turkey.».
• [19] Haykin, S. S. (2009). Neural networks and learning machines/Simon Haykin..
• [20] «OKKAN, U., SERBEŞ, Z. A., & GEDİK, N. (2018). MATLAB ile Levenberg-Marquardt algoritması tabanlı YSA uygulaması: Aylık yağış-akış modellemesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 9(1), 351-362.».
• [21] «Akgun, G. (2015) Data driven predictive control of exoskeleton for hand rehabilitation. Master Thesis, Marmara Universities institute for graduate studies in pure and applied sciences, Istanbul, Turkey.».