LAUSR

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 Defaillances, de leurs Effets et de leur Criticite), 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.