Prognosis and health management plays an important role in the control of costs associated with operating large industrial equipment, such as wind turbines and aircraft. It is only fair that engineers and scientists have vastly researched modeling approaches to support decision making. Motivated by the growing availability of data and computational power as well as the advances in algorithms and methods, modeling frameworks often merge elements of physics, machine learning, and statistical learning. In this paper, we present a review on modeling in support of prognosis and health management of industrial equipment. This survey complements the existing prognosis and health management literature by discussing how modeling strategies are influenced by industry-specific aspects such as maintenance approaches (e.g., reactive, proactive, and predictive), implementation factors (e.g., industry, business model, purpose, development, and deployment), as well as supporting technologies (sensing, repair, and modeling itself). We use the onshore wind energy and civil aviation industries to illustrate how these aforementioned aspects can influence modeling and implementation of prognosis and health management. The literature review is broad and covers contributions over the past 40 years. We close the paper with few topics that can motive research going forward.

预测和健康管理在控制与运行大型工业设备（例如风力涡轮机和飞机）相关的成本方面发挥着重要作用。工程师和科学家对支持决策制定的建模方法进行了大量研究，这是公平的。受数据和计算能力不断提高以及算法和方法进步的推动，建模框架通常融合了物理学、机器学习和统计学习的元素。在本文中，我们对支持工业设备预后和健康管理的建模进行了综述。该调查通过讨论建模策略如何受到行业特定方面的影响，例如维护方法（例如，反应性、主动性和预测性）、实施因素（例如，行业、商业模式、目的、开发和部署）以及支持技术（感知、修复和建模本身）。我们使用陆上风能和民航行业来说明上述这些方面如何影响预测和健康管理的建模和实施。文献综述范围广泛，涵盖了过去 40 年的贡献。我们以一些可以推动研究向前发展的主题结束论文。我们使用陆上风能和民航行业来说明上述这些方面如何影响预测和健康管理的建模和实施。文献综述范围广泛，涵盖了过去 40 年的贡献。我们以一些可以推动研究向前发展的主题结束论文。我们使用陆上风能和民航行业来说明上述这些方面如何影响预测和健康管理的建模和实施。文献综述范围广泛，涵盖了过去 40 年的贡献。我们以一些可以推动研究向前发展的主题结束论文。