Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering. With recent developments in Industry 4.0, machine learning and digital twin, there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process.

In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored. A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technology.

自主已成为许多行业研发的重点。传统上，这是通过在组件级别对自我工程行为进行建模来实现的，而现在，随着人工智能的发展，人们的工作正集中在子系统和系统级别。利用其优势需要一些创新的思想，以整合来自大数据分析，数字化，传感，优化，信息技术和系统工程的总体概念。随着工业4.0，机器学习和数字孪生技术的最新发展，人们越来越有兴趣将这些概念应用于自动维护。直接从运营数据中进行预测性维护计划的自动化，并在系统级进行内置维护。然而，

有鉴于此，重要的是要介绍有关该技术今天的现状的当前观点，并指出未来的可能路线。因此，这项工作着眼于自动维护的最新趋势，然后再从需求的角度讨论数字孪生作为决策工具，同时还探讨了AI在协助此过程中的作用。还讨论了在维护模型中集成数字孪生策略的建议框架。最后，本文着眼于未来的发展方向，探讨其作为可持续技术发展的可能性及其含义。