Intelligent system for monitoring and controlling the technical condition of mechatronic process facilities

Introduction. Digital systems that control the maintenance of separate mechatronic process facilities (MPF) and sets of production machines are mainly considered. Numerous issues on maintaining the reliability of the condition and emerging malfunctions, as well as the multifactorial nature of the using the existing monitoring and diagnostic systems, are noted. In this regard, the relevance of the tasks of developing methods of processing equipment maintenance to make decisions under the data veracity and limitation is specified.

Materials and Methods. To analyze the criticality of the technical condition, an assessment of the efficiency of the autonomous control of the device state is formed. The method of the neuro-fuzzy system is used to determine the aggregate criterion of criticality. It is proposed to apply this approach to develop recommendations on equipping a production facility with the necessary means of maintaining overall performance and reliability.

Results. The solution provides predicting the development of the state of mechatronic process equipment, alerting personnel in case of emergency and other dangerous conditions, and, if necessary, updating or adjusting control programs. Provision is made for performing of some of the technical state maintenance functions by the mechatronic facility itself, i.e., equipment self-service. The concept of “autonomous management of the technical condition” is formulated. The system structure and control functions are considered. It is noted that the implementation of the systems under consideration will significantly increase the efficiency of the equipment use. The performance of the autonomous control of the device or MPF in general is evaluated in accordance with ISO 13381-1: 2004. Based on this standard and the data presented earlier, a neural network structure is built to assess the autonomy of state management. The system training efficiency is considered taking into account the standard deviation of the network outputs from the target values of the training sample.

Discussion and Conclusion. A list of the basic control functions at different levels of maintenance autonomy is presented: from alarm for failure prediction to complete maintenance autonomy without the direct involvement of an operator.

1. Nikitin YuR, Abramov IV. O postroenii sistemy diagnostirovaniya stankov s CHPU [Designing of CNC machines, diagnosing system]. Mechatronics, Automation, Control. 2011;4:32–35. (In Russ.)

2. Mal'tsev AI, Mal'tsev AA. Monitoring tekhnicheskogo sostoyaniya krupnykh mashin [Monitoring the technical condition of large machines]. Elektrostal: DAMO; 1998. 62 p. (In Russ.)

3. Muller A, Suhner M-C, Iung B. Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliability Engineering and System Safety. 2008;93(2):234–253. URL: https://ideas.repec.org/a/eee/reensy/v93y2008i2p234-253.html (accessed 15.05.2020).

4. Nikitin YuR, Abramov IV. O postroenii sistemy diagnostirovaniya stankov s CHPU [Designing of CNC machines, diagnosing system]. Mechatronics, Automation, Control. 2011;4:32–35. (In Russ.)

5. Lee J, Ni J, Djurdjanovic D, et al. Intelligent prognostics tools and e-maintenance. Computers in Industry. 2006;57:476–489.

6. Muller A, Suhner M-C, Iung B. Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliability Engineering and System Safety. 2008;93(2):234–253. URL: https://ideas.repec.org/a/eee/reensy/v93y2008i2p234-253.html (accessed 15.05.2020).

7. Djurdjanovic D, Lee J, Ni J. Watchdog Agent — an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics. 2003;17(3):109–125.

8. Lee J, Ni J, Djurdjanovic D, et al. Intelligent prognostics tools and e-maintenance. Computers in Industry. 2006;57:476–489.

9. Moore WJ, Starr AG. An intelligent maintenance system for continuous cost-based prioritisation of maintenance activities. Computers in Industry. 2006;57:595–606.

10. Djurdjanovic D, Lee J, Ni J. Watchdog Agent — an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics. 2003;17(3):109–125.

11. Kruglova TN, Glebov NA. Diagnostirovanie i prognozirovanie tekhnicheskogo sostoyaniya mekhatronnykh modulei dvizheniya tekhnologicheskogo oborudovaniya [Diagnosis and prediction of the technical condition of mechatronic motion modules of technological equipment]. Novocherkassk: Izd-vo YURGTU (NPI); 2011. P. 119. (In Russ.)

12. Moore WJ, Starr AG. An intelligent maintenance system for continuous cost-based prioritisation of maintenance activities. Computers in Industry. 2006;57:595–606.

13. Emmanouilidis C, Jantunen E, Gilabert E, et al. E-Maintenance update: the road to success for modern industry. COMADEM: рroc of the 24th International Conference on Condition Monitoring and Diagnostic Engineering Management, 30 May - 1 June 2011. Norway, Stavanger. ResearchGate: URL: https://www.researchgate.net/publication/258006515_e-aintenance_update_the_road_to_success_for_modern_industry (accessed 15.05.2020).

14. Kruglova TN, Glebov NA. Diagnostirovanie i prognozirovanie tekhnicheskogo sostoyaniya mekhatronnykh modulei dvizheniya tekhnologicheskogo oborudovaniya [Diagnosis and prediction of the technical condition of mechatronic motion modules of technological equipment]. Novocherkassk: Izd-vo YURGTU (NPI); 2011. P. 119. (In Russ.)

15. Wang P, Vachtsevanos G. Fault prognostics using dynamic wavelet neural networks. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 2001;15:349–365.

16. Emmanouilidis C, Jantunen E, Gilabert E, et al. E-Maintenance update: the road to success for modern industry. COMADEM: рroc of the 24th International Conference on Condition Monitoring and Diagnostic Engineering Management, 30 May - 1 June 2011. Norway, Stavanger. ResearchGate: URL: https://www.researchgate.net/publication/258006515_e-aintenance_update_the_road_to_success_for_modern_industry (accessed 15.05.2020).

17. Wang P, Vachtsevanos G. Fault prognostics using dynamic wavelet neural networks. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 2001;15:349–365.

18. Zhang W, Halang W, Diedrich C. An agent-based platform for service integration in E-maintenance. Industrial technology: proc. of IEEE international conference. Slovenia, Maribor. 2003;1:426–433. ResearchGate: URL: https://www.researchgate.net/publication/4070917_An_agent-based_platform_for_service_integration_in_Emaintenance (accessed 15.05.2020).

19. Zhang W, Halang W, Diedrich C. An agent-based platform for service integration in E-maintenance. Industrial technology: proc. of IEEE international conference. Slovenia, Maribor. 2003;1:426–433. ResearchGate: URL: https://www.researchgate.net/publication/4070917_An_agent-based_platform_for_service_integration_in_Emaintenance (accessed 15.05.2020).

20. Lee J. E-manufacturing: fundamental, tools, and transformation. Robot. Comput.-Integr. Manuf. 2003;19(6):501–507.

21. Lee J. E-manufacturing: fundamental, tools, and transformation. Robot. Comput.-Integr. Manuf. 2003;19(6):501–507.

22. Mitchell J, Bond T, Bever K, et al. MIMOSA — four years later. Journal of Sound and Vibration. 1998;11:12–21.

23. Mitchell J, Bond T, Bever K, et al. MIMOSA — four years later. Journal of Sound and Vibration. 1998;11:12–21.

24. Lee L, Wu F, Zhao W, et al. Prognostics and health management design for rotary machinery systems — reviews, methodology and applications. Mechanical Systems and Signal Processing. 2014;42:314–334.

25. Lee L, Wu F, Zhao W, et al. Prognostics and health management design for rotary machinery systems — reviews, methodology and applications. Mechanical Systems and Signal Processing. 2014;42:314–334.

26. Cao X, Jiang P. Development of SOA Based Equipments Maintenance Decision Support System. Intelligence Robotics and Applications: Proc. First International Conference, ICIRA. China, Wuhan October 15–17, 2008. Berlin; Heidelberg: Springer-Verlag; 2008. Part II;5315:576–582.

27. Cao X, Jiang P. Development of SOA Based Equipments Maintenance Decision Support System. Intelligence Robotics and Applications: Proc. First International Conference, ICIRA. China, Wuhan October 15–17, 2008. Berlin; Heidelberg: Springer-Verlag; 2008. Part II;5315:576–582.

28. Rissland EL, Skalak DB. Combining case-based and rule-based reasoning: A heuristic approach. In: Proc. 11th International joint conference on Artificial intelligence (IJCAI-89). Michigan, Detroit. Vol. 1:524–530. URL: https://www.researchgate.net/publication/220816256_Combining_Case-Based_and_Rule-Based_Reasoning_A_Heuristic_Approach (accessed 15.05.2020).

29. Rissland EL, Skalak DB. Combining case-based and rule-based reasoning: A heuristic approach. In: Proc. 11th International joint conference on Artificial intelligence (IJCAI-89). Michigan, Detroit. Vol. 1:524–530. URL: https://www.researchgate.net/publication/220816256_Combining_Case-Based_and_Rule-Based_Reasoning_A_Heuristic_Approach (accessed 15.05.2020).

30. Bangemann T, Reboul D, Scymanski J, et al. PROTEUS — An integration platform for distributed maintenance systems. Computers in Industry. Special issue on E-maintenance. 2006;57(6):539–551.

31. Bangemann T, Reboul D, Scymanski J, et al. PROTEUS — An integration platform for distributed maintenance systems. Computers in Industry. Special issue on E-maintenance. 2006;57(6):539–551.

32. Karray MH, Chebel-Morello B, Lang C. A component based system for S-maintenance. ResearchGate: URL: https://www.researchgate.net/publication/252048503_A_component_based_system_for_S-maintenance (accessed 15.05.2020).

33. Karray MH, Chebel-Morello B, Lang C. A component based system for S-maintenance. ResearchGate: URL: https://www.researchgate.net/publication/252048503_A_component_based_system_for_S-maintenance (accessed 15.05.2020).

34. Muller A, Marquez C, Iung B. On the concept of e-maintenance: Review and current research. Journal of Reliability Engineering and System Safety. 2008;93:1165–1187.

35. Muller A, Marquez C, Iung B. On the concept of e-maintenance: Review and current research. Journal of Reliability Engineering and System Safety. 2008;93:1165–1187.

36. Tugengol'd AK, Voloshin RN, Yushchenko SV. Modul' E-Mindmachine v intellektual'noi sisteme monitoringa stanka [E-Mindmachine module in the intelligent machine monitoring system]. International Research Journal. 2008;9(40), pt. 2:100–102. (In Russ.)

37. Tugengol'd AK, Voloshin RN, Yushchenko SV. Modul' E-Mindmachine v intellektual'noi sisteme monitoringa stanka [E-Mindmachine module in the intelligent machine monitoring system]. International Research Journal. 2008;9(40), pt. 2:100–102. (In Russ.)

38. Tugengol'd AK, Voloshin RN, Dimitrov VP, et al. Upravlenie tekhnicheskim sostoyaniem stankov [Machine condition management]. STIN. 2018;7:8–15. (In Russ.)

39. Tugengol'd AK, Voloshin RN, Dimitrov VP, et al. Upravlenie tekhnicheskim sostoyaniem stankov [Machine condition management]. STIN. 2018;7:8–15. (In Russ.)

40. Tugengol'd AK, Voloshin RN. Gibkii monitoring mekhatronnykh tekhnologicheskikh mashin [Flexible monitoring of mechatronic technological machines]. Vestnik of DSTU. 2016;4:51–58. (In Russ.)

41. Tugengol'd AK, Voloshin RN. Gibkii monitoring mekhatronnykh tekhnologicheskikh mashin [Flexible monitoring of mechatronic technological machines]. Vestnik of DSTU. 2016;4:51–58. (In Russ.)

42. Tugengol'd AK, Dimitrov VP, Izyumov AI, et al. Monitoring and Control of Tools in Multifunctional Machine Tools. Russian Engineering Research. 2017;37(5):440–446.

43. Tugengol'd AK, Dimitrov VP, Izyumov AI, et al. Monitoring and Control of Tools in Multifunctional Machine Tools. Russian Engineering Research. 2017;37(5):440–446.

44. Tugengol'd AK, Dimitrov VP, Voloshin RN, et al. Monitoring of Machine Tools. Russian Engineering Research. 2017;37(8):440–446.

45. Tugengol'd AK, Dimitrov VP, Voloshin RN, et al. Monitoring of Machine Tools. Russian Engineering Research. 2017;37(8):440–446.

46. Push VE, Pigert R, Sosonkin VL. Avtomaticheskie stanochnye sistemy [Automatic machine systems]. Moscow: Mashinostroenie; 1982. 320 p. (In Russ.)

47. Push VE, Pigert R, Sosonkin VL. Avtomaticheskie stanochnye sistemy [Automatic machine systems]. Moscow: Mashinostroenie; 1982. 320 p. (In Russ.)

48. Tugengol'd AK, Dimitrov VP, Borisova LV, et al. Autonomous Maintenance of Digital Equipment. Russian Engineering Research. 2019;39(6):510–515.

49. Tugengol'd AK, Dimitrov VP, Borisova LV, et al. Autonomous Maintenance of Digital Equipment. Russian Engineering Research. 2019;39(6):510–515.

50. YadchenkoAV, Solomykin MYu, Tugengol'd AK, Voloshin RN. Struktura i algoritm raboty sistemy diagnostiki sostoyaniya mnogooperatsionnogo stanka [The structure and algorithm of the system for diagnosing the state of a multioperational machine]. Sovremennye nauchnye issledovaniya i razrabotki. 2017;8(8):232–237. (In Russ.)

51. YadchenkoAV, Solomykin MYu, Tugengol'd AK, Voloshin RN. Struktura i algoritm raboty sistemy diagnostiki sostoyaniya mnogooperatsionnogo stanka [The structure and algorithm of the system for diagnosing the state of a multioperational machine]. Sovremennye nauchnye issledovaniya i razrabotki. 2017;8(8):232–237. (In Russ.)

52. Tugengol'd AK, Voloshin RN. Kriterii kritichnosti pri analize sostoyaniya tekhnologicheskikh mashin [Criteria of criticality in the analysis of the state of technological machines]. In: Proc. 10th Int. Sci.-Pract. Conf. within framework of the 20th Int. Agroindustrial Exhibition “Interagromash-2017”. Rostov-on-Don: Izd-vo DGTU; 2017. P. 288–292. (In Russ.)

53. Tugengol'd AK, Voloshin RN. Kriterii kritichnosti pri analize sostoyaniya tekhnologicheskikh mashin [Criteria of criticality in the analysis of the state of technological machines]. In: Proc. 10th Int. Sci.-Pract. Conf. within framework of the 20th Int. Agroindustrial Exhibition “Interagromash-2017”. Rostov-on-Don: Izd-vo DGTU; 2017. P. 288–292. (In Russ.)