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

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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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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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  1. ICSF - Laboratory of Intellectual Control System and Forecasting, IICT - Institute of Information and Computing Technologies, Almaty, Kazakhstan

    Galina Samigulina

  2. FIT- Faculty of Information Technologies, KBTU- Kazakh British Technical University, Almaty, Kazakhstan

    Zarina Samigulina

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  1. Galina Samigulina
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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 33, 1433–1450 (2022). https://doi.org/10.1007/s10845-020-01732-5

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