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.

如今，工业企业配备了先进的设备，其状态的诊断和预测是当务之急。本文介绍了基于分析故障模式，其影响和AMDEC严重程度的方法（工业分析模式，de Eurets et de Leur批判）以及改进后的工业设备诊断系统。以TengizChevroil企业的实际生产数据为例的人工免疫系统（AIS）算法。通过使用改进的基于灰狼优化和花粉授粉方法的改进的GWO-AIS和FPA-AIS算法评估设备故障的严重程度，可以改进经典AMDEC模型。