Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance: A Review

Reliability and maintenance serve as pivotal factors in safeguarding safety, enhancing efficiency, optimizing costs, and fostering sustainable development. They permeate all facets of industry, daily life, and society, thereby constituting a crucial foundation for achieving long-term, stable development. The rapid evolution of data-driven methods and artificial intelligence (AI) has revolutionized reliability and maintenance practices, driving a shift from reactive to predictive maintenance (PdM) and ultimately intelligent maintenance strategies. Unlike existing reviews that focus on single technologies or tasks, this paper adopts a system-level integration perspective to construct a closed-loop framework connecting data-driven reliability analysis, maintenance optimization, and intelligent decision-making. It further elucidates the integrated logic between prediction and decision-making through formalized mechanisms. This article systematically reviews the research progress and practical applications of data-driven methods and AI in reliability and maintenance. First, it classifies and summarizes data-driven reliability analysis methods based on existing literature. Second, a reliability-oriented maintenance optimization framework is proposed, comprehensively integrating economic, reliability, resource efficiency, and multi-objective collaboration considerations, while analyzing the characteristics of diverse maintenance systems. Furthermore, the innovative applications and performance advantages of AI algorithms in complex system maintenance are synthesized, and a comparative analysis of the applicability of different methods across various operational scenarios is conducted. And conducted a multidimensional comparison of the applicability scenarios for different methods from an engineering selection perspective. In addition, this review examines the current status and challenges of applying data-driven and AI technologies across multiple real industrial settings and identifies common obstacles encountered during project implementation. We further elucidate the research positioning of this work and provide a comparative discussion with existing review articles. Finally, the article conducts a bibliometric analysis to map the research landscape, provides quantitative support for the development trends in the field. Limitations in this field are also discussed.