An Intelligent Support Method for the Formation of Control Actions in Proactive Management of Complex Systems

This paper addresses the problem of ensuring the continuous operation of cyber–physical systems (CPS) under conditions of component degradation and wear. To achieve this goal, a transition to the concept of Proactive Prognostics and Health Management (PPHM) is proposed, focused on proactive control of the technical condition of equipment. A key stage of PPHM is the generation of control actions aimed at extending the remaining useful life by adapting the operational parameters of the system. This paper proposes an intelligent support method for generating control actions to optimize the operational conditions. The proposed method integrates an RUL prediction model with optimization procedures based on genetic algorithm. The method was experimentally validated using XJTU-SY Bearing test rig and a bearing-degradation dataset. The obtained results demonstrate its effectiveness and confirm its applicability for extending the service life of technical systems. The proposed method is general and can be adapted to any CPS where controllable parameters affect the degradation rate