iipp publishingJournal of Applied Engineering Science


DOI: 10.5937/jaes16-18405
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License

Volume 16 article 560 pages: 515 - 522

Andrey I. Vlasov*
Bauman Moscow State Technical University, Russian Federation
LLC “Konnekt”, Russian Federation

Vladimir V. Echeistov
Bauman Moscow State Technical University, Russian Federation

Aleksey I. Krivoshein
Bauman Moscow State Technical University, Russian Federation
LLC “Konnekt”, Russian Federation

Vadim A. Shakhnov
Bauman Moscow State Technical University, Russian Federation

Sergey S. Filin
LLC “Konnekt”, Russian Federation

Vladimir S. Migalin
LLC “Konnekt”, Russian Federation

The work considers the stages of design and operation of the information expert system of predictive maintenance analytical support of industrial equipment exemplified by vacuum devices. Special attention was paid to the study of the methods of maintenance of the equipment and also to the development of a concept of a modern system of predictive maintenance. The formalization of the test system of predictive maintenance was performed in the package MS Excel using the programming language Visual Basic for Applications. The result of work is the development of an automated expert system of analysis of the methods and means of predictive maintenance of vacuum devices. The particular results were obtained with the support of the Ministry of Education and Science of the Russian Federation within the project of the Agreement No. 14.579.21.0142 UID RFMEFI57917X0142.

View article

Some results of the work were obtained with the support of the project No. 14.579.21.0142 UID RFMEFI57917X0142 within the framework of the Federal Target Program “Research and development in priority areas of development of the scientifi c and technological complex of Russia for 2014-2020”.

1. Mobley R.K. (2002). An Introduction to Predictive Maintenance, Elsevier Science.

2. Curcuru G., Cocconcelli M., Rubini R., and Galante G.M., (2017). System Monitoring and Maintenance Policies: A Review. Proceedings of The International Conference Surveillance 9, 22-24 May 2017, https://iris.unimore.it/handle/11380/1144821

3. Rausand M., and Hoyland A. (2004). System Reliability Theory: Models, Statistical Methods and Applications, Wiley.

4. GOST R 57329-2016 (2016). Systems of Industrial Automation and Integration. Systems of Maintenance and Repair. Terms and Definitions, Publishing House of Standards, Moscow.

5. Lu C.J., and. Meeker W.Q. (1993). Using Degradation Measures to Estimate Time to Failure Distribution. Techno- metrics, 35(2), 161-174.

6. Prause G., and Atari S. (2017). On Sustainable Production Networks for Industry 4.0. Entrepreneurship and Sustainability Issues, 4(4), 421-431, https://doi.org/10.9770/jesi.2017.4.4

7. Akhter F. (2017). Unlocking Digital Entrepreneurship through Technical Business Process. Entrepreneurship and Sustainability Issues, 5(1), 36-42, https://doi.org/10.9770/jesi.2017.5.1

8. Crowder M., and Lawless J. (2007). On a Scheme for Predictive Maintenance. European Journal of Operational Research, 176, 1713-1722.

9. Baptista M., Sankararaman S., Medeiros I.P. de, Nascimento C. Jr., Prendinger H., and Henriques E.M.P. (2018). Forecasting Fault Events for Predictive Maintenance Using Data-Driven Techniques and ARMA Modeling. Computers & Industrial Engineering, 115, 41-53, https://doi.org/10.1016/j.cie.2017.10.033.

10. Whitaker D.A., Egan D., O’Brien E., and Kinnear D. (2018). Application of Multivariate Data Analysis to Machine Power Measurements as a Means of Tool Life Predictive Maintenance for Reducing Product Waste, Available at: https://arxiv.org/abs/1802.08338.

11. Amruthnath N., and Gupta T. (2018). A Research Study on Unsupervised Machine Learning Algorithms for Early Fault Detection in Predictive Maintenance, Available at: https://doi.org/10.13140/RG.2.2.28822.24648.

12. Vlasov A.I., Yudin A.V., Salmina M.A., Shakhnov V.A., and Usov K.A. (2017). Design Methods of Teaching the Development of Internet of Things Components with Considering Predictive Maintenance on the Basis of Mechatronic Devices. International Journal of Applied Engineering Research, 12(20), 9390-9396.

13. Yudin A., Kolesnikov M., Vlasov A., and Salmina M. (2017). Project Oriented Approach in Educational Robotics: From Robotic Competition to Practical Appliance. Advances in Intelligent Systems and Computing, 457, 83-94.

14. Odintsova A.P. (2018). Power Grid Companies Switch to Repair According to Actual Condition, Available at: https://www.clover.global/news/articles/elektrosetevyye-kompanii-perekhodyat-na-remont-po-fakticheskomu-sostoyaniyu.

15. Burduk A., and Chlebus E. (2009). Evaluation of the Risk in Production systems with a Parallel Reliability Structure. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 2(42), 84-95.

16. Bersimis S., Psarakis S., and Panaretos J. (2006). Multivariate Statistical Process Control Charts: An Overview. Quality and Reliability Engineering International, 23(5), 517-543.

17. Vermesan O., and Friess P. (2014). Internet of Things – From Research and Innovation to Market Deployment. River Publishers, pp. 106-112.

18. Goebel K., Daigle M., Saxena A., Sankararaman S., and Celaya I.R. (2017). Prognostics: The Science of Making Predictions, CreateSpace Independent Publishing Platform.

19. Bakdi A., Kouadri A., and Bensmail A. (2017). Fault Detection and Diagnosis in a Cement Rotary Kiln Using PCA with EWMA-Based Adaptive Threshold Monitoring Scheme. Control Engineering Practice, 66, 64-75.

20. Lebold M., and Thurston M. (2001). Open Standards for Condition-Based Maintenance and Prognostic Systems. Proceedings of MARCON 2001 – Fifth Annual Maintenance and Reliability Conference, Gatlinburg, USA.

21. Yang J., Chen Y., and Sun Z. (2017). A Real-Time Fault Detection and Isolation Strategy for Gas Sensor Arrays. Instrumentation and Measurement Technology Conference (I2MTC), 2017 IEEE International, 22-25 May 2017, https://doi.org/10.1109/I2MTC.2017.7969906.

22. Liggan P., and Lyons D. (2011). Applying Predictive Maintenance Techniques to Utility Systems, Retrieved from Pharmaceutical Engineering. Official Magazine of ISPE, 31(6), 1-7.

23. Du Z., Fan B., Jin X., and Chi J. (2014). Fault Detection and Diagnosis for Buildings and HVAC Systems Using Combined Neural Networks and Subtractive Clustering Analysis. Building and Environment, 73, 1-11.

24. Shafi U., Safi A., Shahid A.R., Ziauddin S., and Saleem M.Q. (2018). Vehicle Remote Health Monitoring and Prognostic Maintenance System. Journal of Advanced Transportation, Article ID 8061514, Available at: https://doi.org/10.1155/2018/8061514.

25. Samanpour A.R., Ruegenberg A., and Ahlers R. (2017). The Future of Machine Learning and Predictive Analytics. Digital Marketplaces Unleashed, Springer, Available at: https://doi.org/10.1007/978-3-662-49275-8_30.

26. Emelianov S.G., Bakaeva N.V., and Gordon V.A. (2017). A multi-level scale of technical safety indicators of urban life support systems. Journal of Applied Engineering Science, 15(4), 459-462, https://doi.org/10.5937/jaes15-15451.

27. Rakonjac I.M., Rakonjac I.M., and Gasic M.P. (2017). Comparative review of the risk assessment quantitative models for public open spaces lighting design optimization. Journal of Applied Engineering Science, 15(2), 181-186, https://doi.org/ 10.5937/jaes15-13926.

28. Farajpourbonab E. (2017). Effective parameters on the behavior of CFDST columns. Journal of Applied Engineering Science, 15(1), 99-108, https://doi.org/10.5937/jaes15-12474.