With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the Internet of Things, Artificial Intelligence, machine learning, and real-time big data analytics, there is a unique opportunity for efficient predictive maintenance to forecast equipment failures for real-time intervention and optimize maintenance actions, as traditional reactive and preventive maintenance practices are often inadequate to meet the requirements for the industry to provide quality-of-services of operations. Central to this evolution is digital twin technology, an adaptive virtual replica that continuously monitors and integrates sensor data to simulate and improve asset performance. Despite remarkable progress in digital twin implementations, such as considering DT in predictive maintenance for industrial engineering. This paper aims to address this void. We perform a retrospective analysis of the temporal evolution of the digital twin in predictive maintenance for industrial engineering to capture the applications, middleware, and technological requirements that led to the development of the digital twin from its inception to the AI-enabled digital twin and its self-learning models. We provide a layered architecture of the digital twin technology, as well as a taxonomy of the technology-enabled industrial engineering applications systems, middleware, and the used Artificial Intelligence algorithms. We provide insights into these systems for the realization of a trustworthy and efficient smart digital-twin industrial engineering ecosystem. We discuss future research directions in digital twin for predictive maintenance in industrial engineering.

翻译：随着工业系统复杂性的日益增加，预测性维护的需求愈发迫切，以避免代价高昂的停机时间以及在特定领域中可能危及生命的灾难性后果。在物联网、人工智能、机器学习和实时大数据分析技术日益普及的背景下，高效的预测性维护迎来了独特的发展机遇，能够预测设备故障以实现实时干预并优化维护策略。传统的被动式与预防性维护方法往往难以满足工业运营对服务质量的要求，而数字孪生技术正是这一变革的核心——它是一种自适应虚拟副本，通过持续监测并整合传感器数据来模拟和优化资产性能。尽管数字孪生在工业工程预测性维护等领域的应用已取得显著进展，现有研究仍缺乏系统性梳理。本文旨在填补这一空白。我们通过回顾性分析数字孪生在工业工程预测性维护中的时序演进，梳理了从技术萌芽到人工智能增强型数字孪生及其自学习模型发展过程中所涉及的应用场景、中间件与技术需求。本文提出了数字孪生技术的分层架构体系，并对技术赋能的工业工程应用系统、中间件及采用的人工智能算法进行了分类归纳。我们深入剖析了这些系统如何构建可信高效的数字孪生智能工业工程生态体系，并探讨了数字孪生在工业工程预测性维护领域的未来研究方向。