关键词: 装备可靠性, 不确定性量化, 不确定性控制, 鲁棒性, 韧性

Abstract: As modern equipment systems continue to evolve towards greater complexity, intelligence, and networking, the uncertainties throughout their life cycles have shifted from being singular, local, and static to being multi-source, dynamic, and highly coupled. Traditional reliability theories and methods based on failure statistics and static assessment are facing a fundamental restructuring of their applicability boundaries. Focusing on the reliability of complex equipment, this paper systematically reviews the state of the art and progress in uncertainty quantification and uncertainty control, with an emphasis on the intrinsic logic behind the paradigm shift of modern reliability engineering from "failure analysis" to a closed-loop paradigm of "uncertainty cognition – propagation analysis – active regulation". First, we trace the evolution of the connotation of equipment reliability, clarify how robustness and resilience extend the traditional concept of reliability under complex uncertainty, and analyze the classification of uncertainties in equipment systems and their impacts on reliability and safety. Second, we summarize key methods and applications in uncertainty quantification, as well as representative strategies for uncertainty control. Furthermore, we discuss the supporting roles and integration pathways of key enabling technologies—such as artificial intelligence, digital twins, and socio-technical systems engineering—in the fusion of uncertainty quantification and control. Finally, we provide an outlook on directions including deep integration of trustworthy AI, real-time online and adaptive uncertainty quantification and control, uncertainty governance of complex systems-of-systems, and holistic reliability assurance under human–machine–organization synergy. The study shows that the core mission of complex equipment reliability research is shifting from passive failure assessment to proactive governance of uncertainties and their risk evolution—a transformation that will serve as a major driving force for theoretical innovation and technological breakthroughs in equipment reliability engineering.

Key words: equipment reliability, uncertainty quantification, uncertainty control, robustness, resilience