Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems

Abstract

Nowadays, industrial enterprises are equipped with sophisticated equipment, diagnostics and prediction of the state of which is an urgent task. The article presents the developed system for diagnostics of industrial equipment based on the methodology for analyzing failure modes, their influence and the degree of AMDEC criticality (l'Analyse des Modes de Défaillances, de leurs Effets et de leur Criticité), as well as modified algorithms of artificial immune systems (AIS) on the example of real production data of TengizChevroil enterprise. The classical AMDEC model is improved by assessing the degree of criticality of equipment failures using the developed modified GWO-AIS and FPA-AIS algorithms based on gray wolf optimization and flower pollination methods. The proposed diagnostic system allows to reduce the financial risks of an enterprise associated with equipment faults by predicting possible failures, the possibility of planning maintenance, reducing the time for equipment repair and increasing the reliability of production.

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References

  1. Abdel-Basset, M. (2019). Flower pollination algorithm: A comprehensive review. Artificial Intelligence Review, 52, 2533–2557.

    Article  Google Scholar 

  2. Ataser, Z. (2013). Review of artificial immune systems. In International conference on computational intelligence (pp. 128–135).

  3. Aydin, I., Karakose, M., & Akin, E. (2010). Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection. Expert Systems with Application, 37, 5285–5294.

    Article  Google Scholar 

  4. Aydin, I., Karakose, M., Karakose, E., & Akin, E. (2018). A new fault diagnosis approach for induction motor using negative selection algorithm and its real-time implementation on FPGA. Journal of Intelligent and Fuzzy Systems, 34(1), 689–701.

    Article  Google Scholar 

  5. Bahekar, K. B., & Gupta, A. K. (2018). Artificial immune recognition system-based classification technique. In Proceedings of international conference on recent advancement on computer and communication. Lecture Notes in Networks and Systems (Vol. 34, pp. 629–635).

  6. Bayara, N., Darmoulb, S., Hajri-Gabouja, S., & Pierrevalc, H. (2015). Fault detection, diagnosis and recovery using artificial immune systems: A review. Engineering Applications of Artificial Intelligence, 46, 43–57.

    Article  Google Scholar 

  7. Cabarbaye, A. (2017). Sûreté de Fonctionnement & Optimisation des systèmes. Toulouse, France. ISBN 979-10-97287-03-0.

  8. Carlson, C. S. (2012). Effective FMEAs. Achieving safe, reliable, and economical products and processes using failure mode and effects analysis. London: Willey. ISBN 978–1118007433.

  9. Chang, L., Wang, H., & Wang, L. (2013). Fault detection and diagnosis of an HVAC system using artificial immune recognition system. In International conference on Asia-Pacific power and energy engineering (pp. 1–5).

  10. Cui, W., He, Y., Zhao, D., & Zhu, J (2018). A novel binary chaotic cloud flower pollination algorithm for analog fault diagnosis. In International conference on advanced information technology, electronic and automation control (pp. 1175–1179).

  11. Davis, G., Perhinschi, M., & Moncayo, H. (2010). Evolutionary algorithm for artificial-immune-system-based failure-detector generation and optimization. Journal of Guidance, Control and Dynamics, 33(2), 305–320.

    Article  Google Scholar 

  12. Dmitriev, A. Y., & Mitroshkina, T. A. (2012). Brief guidelines for FMEA. New Quality , 2012, 1–22.

    Google Scholar 

  13. Durdjanovic, D., Liu, J., Marko, K., & Ni, J. (2010). Immune systems inspired approach to anomaly detection, fault localization and diagnosis in automotive engines. Applications of Neural Networks in High Assurance Systems, 268, 141–163.

    Article  Google Scholar 

  14. Elfelsoufi, Z., & Sefyani, N. (2015). Analyze des modes de Défaillance de leurs Effets et de leur Criticité des Machines dans une démarche de qualité et de maintenance. International Journal of Innovation and Scientific Research, 13(2), 628–635.

    Google Scholar 

  15. Faris, I., Al-Bear, M., & Mirjalili, S. (2018). Grey wolf optimizer: A review of recent variants and applications. Neural Computing and Applications, 30, 413–435.

    Article  Google Scholar 

  16. Gao, X., Wnag, X., Zenger, K., & Wang, X. (2011). Negative selection algorithm-based motor fault diagnosis. Practical Application of Intelligent Systems, 124, 173–183.

    Article  Google Scholar 

  17. Guidelines for automation solutions. (2011). Practical aspects of process control systems. Paris: Schneider Electric.

    Google Scholar 

  18. Haq, A., & Durdjanovic, D. (2015). Precedent-free fault localization and diagnosis for high speed train drive systems. Facta Universitatis, 13(2), 67–79.

    Google Scholar 

  19. Hudaib, A., Masadeh, R., & Alzaqebah, A. (2018). WGW: A hybrid approach based on whale and grey Wolf optimization algorithms for requirements prioritization. Advanced System Scientific Application, 2, 63–83.

    Google Scholar 

  20. Jegadeeshwaran, R., & Sugumaran, V. (2015). Brake fault diagnosis using clonal selection classification algorithm (CSCA)—A statistical learning approach. Engineering Science and Technology, 18(1), 14–23.

    Google Scholar 

  21. KTL. (2015). TCO complex coordinator daily report as of 11-January-2015.

  22. Kumar, B., & Parhi, D. (2019). Analysis of hybrid CSA-DEA method for fault detection of cracked structures. Journal of Theoretical and Applied Mathematics, 57, 369–382.

    Google Scholar 

  23. Laurentys, C., Ronacher, G., Palhares, R., & Caminhas, W. (2010). Design of an artificial immune system for fault detection: A negative selection approach. Expert Systems with Application, 37(7), 5507–5513.

    Article  Google Scholar 

  24. Li, D., Liu, S., & Zhang, H. (2015). Negative selection algorithm with constant detectors for anomaly detection. Applied Soft Computing, 36, 618–632.

    Article  Google Scholar 

  25. Li, G., Yang, M., & Zhuang, J. (2012). An artificial immune inspired hybrid classification algorithm and its application to fault diagnosis. Advanced Material Research, 411, 626–629.

    Article  Google Scholar 

  26. Lima, F., Lotufo, A., & Minussi, C. (2015). Wavelet-artificial immune system algorithm applied to voltage disturbance diagnosis in electrical distribution systems. Generation, Transmission and Distribution, 9(11), 1104–1111.

    Article  Google Scholar 

  27. Mahapatra, C., Payal, A., & Chopra, M. (2020). Swarm intelligence based centralized clustering: a novel solution. Journal of Intelligent Manufacturing, 2020, 1–12.

    Google Scholar 

  28. Mehmet, I., & Akin, K. (2011). A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Applied Soft Computing, 11(1), 120–129.

    Article  Google Scholar 

  29. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  30. Mohapatra, S., Khilar, M., & Swain, R. (2019). Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach. International Journal of Communication Systems, 2019, 1–20.

    Google Scholar 

  31. Mojtahedi, A., Lotfollahi Yaghin, M. A., Hassanzadeh, Y., Abbasidoust, F., Ettefagh, M. M., & Aminfar, M. H. (2012). A robust damage detection method developed for offshore jacket platforms using modified artificial immune system algorithm. China Ocean Engineering, 26(3), 379–395.

    Article  Google Scholar 

  32. Permanent Technological Regulations on the Process of Extracting LHG on Unit 700. (2017). TengizChevroil, TP-ZVP-700-11.

  33. Sakthivel, N. R., Nair, B. B., Sugumaran, V., & Rai, R. S. (2011). Decision support system using artificial immune recognition system for fault classification of centrifugal pump. International Journal of Data Analysis Techniques and Strategies, 3(1), 66–84.

    Article  Google Scholar 

  34. Samigulina, G. A. (2012). Development of decision support systems based on intellectual technology of artificial immune systems. Automatic and Remote Control, 73, 397–403.

    Article  Google Scholar 

  35. Samigulina, G. A., & Samigulina, Z. I. (2015). Industrial implementation of the immune network modeling of complex objects on the equipment Schneider Electric and Siemens. In International congress on systems immunology, immunoinformatics and immune-computation, July 17–18, Italy, Taormina (pp. 1–9).

  36. Samigulina, G., & Samigulina, Z. (2019a). Development of smart technology for complex objects prediction and control on the basis of a distributed control system and an artificial immune systems approach. Advances in Science, Technology and Engineering Systems Journal, 4(3), 75–87.

    Article  Google Scholar 

  37. Samigulina, G. A., & Samigulina, Z. I. (2019b). Modified immune network algorithm based on the Random Forest approach for the complex objects control. Artificial Intelligence Review, 52, 2457–2473.

    Article  Google Scholar 

  38. Samigulina, G. A., & Samigulina Z. I. (2020). Development of industrial equipment diagnostics based on modified algorithms of artificial immune systems and AMDEC approach using Schneider Electric equipment. In International conference on industrial engineering. Applications and manufacturing (pp. 1–5).

  39. Silva, C., Palhares, G., & Caminhas, R. (2012). Immune inspired fault detection and diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering. Expert Systems with Applications, 39(16), 12474–12486.

    Article  Google Scholar 

  40. Silva, G., Caminhas, W., & Palhares, R. (2017). Artificial immune systems applied to fault detection and isolation: A brief review of immune response-based approaches and a case study. Applied Soft Computing, 57, 118–131.

    Article  Google Scholar 

  41. Tang, P., Gan, Z., Chow, T. (2011). Clonal selection programming for rotational machine fault classification and diagnosis. In International conference on prognostics and system health management (pp. 1–6).

  42. Tarakanov, A. (1999). Formal peptide as a basic of agent of immune networks: from natural prototype to mathematical theory and applications. In International conference of Central and Eastern Europe on Multi-agent Systems (p. 37).

  43. Tarakanov, A., & Nicosia, G. (2007). Foundations of immunocomputing. In International conference on foundations of computational intelligence (pp. 503–508).

  44. Technological regulations of Unit 700, CTL2. (2014). TengizChevroil LLP, TP-KTL-1-700-13.

  45. Vedova, M., Germana, A., Berri, P., & Maggiore, P. (2019). Model-based fault detection and identification for prognostics of electromechanical actuators using genetic algorithms. Aerospace, 6(9), 1–15.

    Google Scholar 

  46. Wu, Q., Ding, K., & Huang, B. (2020). Approach for fault prognosis using recurrent neural network. Journal of Intelligent Manufacturing, 31, 1621–1633.

    Article  Google Scholar 

  47. Yang, X.-S. (2012). Flower pollination algorithm for global optimization. Lecture Notes in Computer Science, 7445, 240–249.

    Article  Google Scholar 

  48. Yang, X.-S. (2014). Flower pollination algorithms. Nature-Inspired Optimization Algorithms, 2014, 155–173.

    Article  Google Scholar 

  49. Yao, F., Wang, F., & Zhang, M. (2018). Weak thruster fault detection for autonomous underwater vehicle based on artificial immune and signal pre-processing. Advances in Mechanical Engineering, 10(2), 1–13.

    Google Scholar 

  50. Yu, Z. J., & Xu, Y. N. (2015). Research of sensor fault detection and diagnosis for EMB system based on CSA-SVM model. International Journal of Engineering and Technology, 7(4), 349–356.

    Article  Google Scholar 

  51. Zhang, H., Liu, J., Zhou, E., Li, D., Wang, B., & Shi, K. (2015). An intelligent fault diagnosis method using variable weight artificial immune recognizers (V-AIR). Journal of Vibroengineering, 17(5), 2350–2368.

    Google Scholar 

  52. Zhang, H., Liu, S., Jiao, W., Li, D., & Wang, B. (2013). The machine abnormal degree detection method based on SVDD and negative selection mechanism. Journal of Vibroengineering, 15(4), 1873–1884.

    Google Scholar 

  53. Zhang, W. (2016). Fault diagnosis method based on artificial immune system. Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines, 12016, 93–2017.

    Google Scholar 

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Acknowledgements

The work was carried out under the Grant No. AP09258508 of the Ministry of Education and Science of the Republic of Kazakhstan on the topic: “Development of intelligent technology for complex objects control based on a unified artificial immune system for industrial automation using modern microprocessor technology”. The authors are grateful to the former director of the Kazakh-French Center (KazFETS) François Girault; Daniel Guyonvarch (Paris, France), instructor for industrial equipment of Schneider Electric, Carlos Canudas-de-Vite director of research at the CNRS, Gipsa-Lab (Grenoble, France) and associate professor Hassen Fourati at the Networked Controlled Systems Team (NeCS), Department of Automatic Control, GIPSA-Lab (Grenoble, France) for his scientific internship.

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Correspondence to Zarina Samigulina.

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Samigulina, G., Samigulina, Z. Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems. J Intell Manuf (2021). https://doi.org/10.1007/s10845-020-01732-5

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Keywords

  • Artificial immune systems
  • Modified algorithms
  • Technical diagnostics
  • Fault prediction
  • AMDEC