Predictive maintenance – a major field for the application of computer aided systems

2022;
: pp. 49 - 56
1
Lviv Polytechnic National University, Ukraine
2
State enterprise “Lviv radio engineering research institute”
3
AGH University of Science and Technology, Krakow MP, Poland

Predictive maintenance is a widely applied maintenance program that requires extensive support of computer aided systems. The program uses specific procedures that are to be addressed when developing predictive maintenance software solutions. Despite the fact that software solutions for predictive maintenance were introduced almost at the same time as the program emerged, it still remains a very actual field for the application of computer aided systems. The practice also shows that developers of computer aided systems for predictive maintenance constantly encounter problems, trying to translate predictive maintenance procedures into the computer language. These procedures are very specific and require microprocessor-based equipment and development of sophisticated algorithms. Therefore, there is a distinct need for better awareness about the predictive maintenance concept among software developers.

The article aims to describe the essence of the predictive maintenance concept, its fundamental approaches and the main physical processes upon which the predictive maintenance procedures are based:

  • (1) distinct vibration frequency components which are inherent in all common failure modes;
  • and (2) constant amplitude of each distinct vibration component.

The importance of the awareness with the concept for computer aided systems developers is emphasized. And the most problematic areas of software application in predictive maintenance programs are outlined, namely the development of effective computerized systems to capture and analyze an immense quantity of data (big data processing), and the development of systems, supporting an intelligent connection of smart devices with the means of internet protocols (Internet of Things).

  1. Thomas É., Levrat É., Iung B., Cocheteux P. (2009). Opportune maintenance and predictive maintenance decision support. IFAC Proceedings Volumes, Volume 42, Issue 4, 1603-1608. https://doi.org/10.3182/20090603-3-RU-2001.0368.
  2. Werner A., Zimmermann N., Lentes J. (2019). Approach for a Holistic Predictive Maintenance Strategy by Incorporating a Digital Twin. 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, August 9-14, Chicago, Illinois (USA), 1743- 1750.
  3. Efthymiou K., Papakostas N., Mourtzis D., Chryssolouris G. (2012). On a Predictive Maintenance Platform for Production Systems. 45th CIRP Conference on Manufacturing Systems 2012, Procedia CIRP 3- 221-226. https://doi.org/10.1016/j.procir.2012.07.039.
  4. Ren S., Zhao X. (2015). A predictive maintenance method for products based on big data analysis. International Conference on Materials Engineering and Information Technology Applications  (MEITA 2015), Atlantis Press, 385-390.
  5. Mobley K. R. (2002). An Introduction to Predictive Maintenance, Second Edition. Butterworth- Heinemann, Elsevier Science, Woburn, MA (USA). 459 p.
  6. Jun H. B., Shin J. H., Kiritsis, D., Xirouchakis, P. (2007). System architecture for closed-loop PLM. International Journal of Computer Integrated Manufacturing, Vol.20, 684-698. https://doi.org/10.1080/09511920701566624
  7. Hashemian H. M., Bean Wendell C. (2011). State-of-the-Art Predictive Maintenance Techniques. IEEE Transaction on Instrumentation and Measurement, Vol. 60, No. 10, 3480-3492. DOI: 10.1109/TIM.2009.2036347.
  8. Hanly S. (2016). Vibration Analysis: FFT, PSD, and Spectrogram Basics. Retrieved from: https://blog.endaq.com/vibration-analysis-fft-psd-and-spectrogram.
  9. Schroeder R. (2016). Big data business models: Challenges and opportunities. Cogent Social Sciences, 2:1, 1166924, 1-15. https://doi.org/10.1080/23311886.2016.1166924.
  10. What is IoT? Retrieved from: www.oracle.com/internet-of-things/what-is-iot.
  11. Parpala R. C., Iacob R. (2017). Application of IoT concept on predictive maintenance of industrial equipment.          MATEC           Web           of           Conferences,          Volume            121,          1-8.https://doi.org/10.1051/matecconf/201712102008.
  12. Albert M. (2015). 7 Things to Know about the IIoT and Industry 4.0. Modern Machine Shop Magazine, Retrieved from: www.mmsonline.com/articles/7-things-to-know-about-the-internet-of-things- and-industry-40.
  13. Dinter R. van, Tekinerdogan B., Cagatay C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008. Retrieved from: www.sciencedirect.com/journal/information-and-software-technology.
  14. Basingab M., Rabelo L., Nagadi K., Rose C., Gutiérrez E., Jung W. I. (2017). Business Modeling Based on Internet of Things: A Case Study of Predictive Maintenance Software Using ABS. ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing, 1-5. http://dx.doi.org/10.1145/3018896.3018905.
  15. Smith R. (2019). Predictive Maintenance / Condition Based Maintenance - "The Facts". Retrieved from:       www.linkedin.com/pulse/predictive-maintenance-condition-based-facts-ricky-smith-cmrp-cmrt.
  16. Narrowband vs. Broadband (2022). Retrieved from: www.sparkfun.com/news/4664.
  17. Sullivan G. P., Pugh R., Melendez A. P., Hunt W. D. (2010). Operations & Maintenance Best Practices: A Guide to Achieving Operational Efficiency. FEDERAL ENERGY MANAGEMENT PROGRAM, Release 3.0, Efficiency Solutions, LLC., 321 p.
  18. Sikorska, J.Z., Hodkiewicz, M., Ma, L. (2011). Prognostic modeling options for remaining useful life estimation by industry, Mechanical Systems & Signal Processing, Volume 25, Issue 5, 1803-1836. https://doi.org/10.1016/j.ymssp.2010.11.018.
  19. Modeling and simulation for product lifecycle integration and management. (2002). Whitepaper. USA: IMTI, Inc.