Skip to main content

Advertisement

Cloud Internet of Things Based Machine Monitoring Analysis of Energy Parameters Using Novel Techniques

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In remote area industrial systems, energy consumption monitoring is a crucial challenge. As the conventional monitoring methods lack an intelligent approach, the finest energy consumption monitoring is not possible. Hence, Internet of Things (IoT) based monitoring methods have been developed by recent industrial systems. Therefore, in this research, a novel cloud with IoT based energy monitoring technique is developed. The energy parameters of the Computer Numerical Control based milling machine has been gathered using IoT based Current Transducers , Voltage Transducers , and power sensors. The IoT device includes Zigbee or Bluetooth for managing communication between the machine and the monitoring system. Then the obtained data is stored in the cloud storage platform for large scale machine energy data in the windows platform. Later on, the obtained data from cloud storage is processed by the novel Normalized Recursive Least Kalman Filter for event detection processing. Moreover, the feature extraction has been done using the proposed Simplified Principal Component Analysis method. Furthermore, the energy utilization of the machine is monitored over various situations using the proposed novel Dynamic Self-evolving Reasoning based Fuzzy Neural algorithm. The Median Absolute Deviation is estimated for the conditional inference of the system. The software implementation of this work is done in MATLAB. The power consumption of the machine is validated under various cases. Besides, the proposed simulation outcomes are compared with various existing energy monitoring systems for verifying the significance of the developed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Khanna, A., & Kaur, S. (2020). Internet of Things (IoT), applications and challenges: A comprehensive review. Wireless Personal Communications, 114, 1687–1762. https://doi.org/10.1007/s11277-020-07446-4

    Article  Google Scholar 

  2. Balaji, S., Nathani, K., & Santhakumar, R. (2019). IoT technology, applications and challenges: A contemporary survey. Wireless Personal Communications, 108, 363–388. https://doi.org/10.1007/s11277-019-06407-w

    Article  Google Scholar 

  3. Malche, T., Maheshwary, P., & Kumar, R. (2019). Environmental monitoring system for smart city based on secure internet of Things (IoT) architecture. Wireless Personal Communications, 107, 2143–2172. https://doi.org/10.1007/s11277-019-06376-0

    Article  Google Scholar 

  4. Iluore, O. E., Onose, A. M., & Emetere, M. (2020). Development of asset management model using real-time equipment monitoring (RTEM): Case study of an industrial company. Cogent Business & Management. https://doi.org/10.1080/23311975.2020.1763649

    Article  Google Scholar 

  5. Tamburri, D. A., Miglierina, M., & Nitto, E. D. (2020). Cloud applications monitoring: An industrial study. Information and Software Technology, 127, 106376. https://doi.org/10.1016/j.infsof.2020.106376

    Article  Google Scholar 

  6. da Silva, F. S. T., da Costa, C. A., Crovato, C. D. P., & da Rosa Righi, R. (2020). Looking at energy through the lens of Industry 4.0: A systematic literature review of concerns and challenges. Computers & Industrial Engineering, 143, 106426. https://doi.org/10.1016/j.cie.2020.106426

    Article  Google Scholar 

  7. Shin, S. J., Woo, J., Kim, D. B., Kumaraguru, S., & Rachuri, S. (2016). Developing a virtual machining model to generate MTConnect machine-monitoring data from STEP-NC. International Journal of Production Research, 54(15), 4487–4505. https://doi.org/10.1080/00207543.2015.1064182

    Article  Google Scholar 

  8. Reddy, V. D., Gangadharan, G. R., & Rao, G. S. V. R. K. (2019). Energy-aware virtual machine allocation and selection in cloud data centers. Soft Computing, 23, 1917–1932. https://doi.org/10.1007/s00500-017-2905-z

    Article  Google Scholar 

  9. Xiao, Y., Jiang, Z., Gu, Q., Yan, W., & Wang, R. (2021). A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost. Journal of Manufacturing Systems, 59, 535–548. https://doi.org/10.1016/j.jmsy.2021.03.023

    Article  Google Scholar 

  10. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94, 3563–3576. https://doi.org/10.1007/s00170-017-0233-1

    Article  Google Scholar 

  11. El Maraghy, H. A., Youssef, A. M. A., Marzouk, A. M., & El Maraghy, W. H. (2017). Energy use analysis and local benchmarking of manufacturing lines. Journal of Cleaner Production, 163, 36–48. https://doi.org/10.1016/j.jclepro.2015.12.026

    Article  Google Scholar 

  12. Yuan, J., Shao, H., Cai, Y., & Shi, X. (2021). Energy efficiency state identification of milling processing based on EEMD-PCA-ICA. Measurement, 174, 109014. https://doi.org/10.1016/j.measurement.2021.109014

    Article  Google Scholar 

  13. Tien, D. H., Duc, Q. T., Van, T. N., Nguyen, N.-T., Duc, T. D., & Duy, T. N. (2021). Online monitoring and multi-objective optimisation of technological parameters in high-speed milling process. The International Journal of Advanced Manufacturing Technology, 112, 2461–2483. https://doi.org/10.1007/s00170-020-06444-x

    Article  Google Scholar 

  14. Gomes, M. C., Brito, L. C., da Silva, M. B., & Duarte, M. A. V. (2021). Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors. Precision Engineering, 67, 137–151. https://doi.org/10.1016/j.precisioneng.2020.09.025

    Article  Google Scholar 

  15. Dutta, S., Pal, S. K., & Sen, R. (2016). Tool condition monitoring in turning by applying machine vision. ASME Journal of Manufacturing Science & Engineering, 138(5), 051008. https://doi.org/10.1115/1.4031770

    Article  Google Scholar 

  16. Morris, A., Baglee, D., & Knowles, M. (2020). Using energy consumption profiles as an indicator of equipment condition. In A. Ball, L. Gelman, & B. Rao (Eds.), Advances in asset management and condition monitoring smart innovation, systems and technologies. Cham: Springer. https://doi.org/10.1007/978-3-030-57745-2_109

    Chapter  Google Scholar 

  17. Ding, K., Zhang, Y., Chan, F. T. S., Zhang, C., Lv, J., Liu, Q., Leng, J., & Fu, H. (2021). A cyber-physical production monitoring service system for energy-aware collaborative production monitoring in a smart shop floor. Journal of Cleaner Production, 297, 126599. https://doi.org/10.1016/j.jclepro.2021.126599

    Article  Google Scholar 

  18. Kuntoğlu, M., Aslan, A., Pimenov, D. Y., Usca, Ü. A., Salur, E., Gupta, M. K., Mikolajczyk, T., Giasin, K., Kapłonek, W., & Sharma, S. (2021). A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors, 21(1), 108. https://doi.org/10.3390/s21010108

    Article  Google Scholar 

  19. Goodall, P., Pantazis, D., & West, A. (2020). A cyber physical system for tool condition monitoring using electrical power and a mechanistic model. Computers in Industry, 118, 103223. https://doi.org/10.1016/j.compind.2020.103223

    Article  Google Scholar 

  20. He, Y., Wu, P., Li, Y., Wang, Y., Tao, F., & Wang, Y. (2020). A generic energy prediction model of machine tools using deep learning algorithms. Applied Energy, 275, 115402. https://doi.org/10.1016/j.apenergy.2020.115402

    Article  Google Scholar 

  21. Li, X. X., He, F. Z., & Li, W. D. (2019). A cloud-terminal-based cyber-physical system architecture for energy efficient machining process optimization. Journal of Ambient Intelligence and Humanized Computing, 10, 1049–1064. https://doi.org/10.1007/s12652-018-0832-1

    Article  Google Scholar 

  22. Liu, W., Kong, C., Niu, Q., Jiang, J., & Zhou, X. (2020). A method of NC machine tools intelligent monitoring system in smart factories. Robotics and Computer-Integrated Manufacturing, 61, 101842. https://doi.org/10.1016/j.rcim.2019.101842

    Article  Google Scholar 

  23. Kang, H. S., Lee, J. Y., & Lee, D. Y. (2020). An integrated energy data analytics approach for machine tools. IEEE Access, 8, 56124–56140. https://doi.org/10.1109/ACCESS.2020.2981696

    Article  Google Scholar 

  24. Pereira, O., Urbikaín, G., Rodríguez, A., Calleja, A., Ayesta, I., & López de Lacalle, L. N. (2019). Process performance and life cycle assessment of friction drilling on dual-phase steel. Journal of Cleaner Production, 213, 1147–1156. https://doi.org/10.1016/j.jclepro.2018.12.250

    Article  Google Scholar 

  25. Wang, Q., & Yang, H. (2020). Sensor-based recurrence analysis of energy efficiency in machining processes. IEEE Access, 8, 18326–18336. https://doi.org/10.1109/ACCESS.2020.2968172

    Article  Google Scholar 

  26. Hu, L., Zheng, H., Shu, L., Jia, S., Cai, W., & Xu, L. (2020). An investigation into the method of energy monitoring and reduction for machining systems. Journal of Manufacturing Systems, 57, 390–399. https://doi.org/10.1016/j.jmsy.2020.10.012

    Article  Google Scholar 

  27. He, Y., Wu, P., Wang, Y., Tao, F., & Hon, B. K. K. (2020). An OPC UA based framework for predicting energy consumption of machine tools. Procedia CIRP, 90, 568–572. https://doi.org/10.1016/j.procir.2020.02.133

    Article  Google Scholar 

  28. Feng, M., Hua, Z., Qingshan, G., & Hon, K. K. B. (2019). A novel energy evaluation approach of machining processes based on data analysis. Recovery Utilization and Environmental Effects Energy Source Part A. https://doi.org/10.1080/15567036.2019.1670761

    Article  Google Scholar 

Download references

Acknowledgements

None

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ankit Agarwal.

Ethics declarations

Conflict of interest

The authors declare that they have no potential conflict of interest.

Ethical Approval

All applicable institutional and/or national guidelines for the care and use of animals were followed.

Informed Consent

For this type of study formal consent is not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, A. Cloud Internet of Things Based Machine Monitoring Analysis of Energy Parameters Using Novel Techniques. Wireless Pers Commun 124, 1789–1814 (2022). https://doi.org/10.1007/s11277-021-09431-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09431-x

Keywords