Prognostics and health management has become a significant part of component life-cycle in modern industries. The prognostics and health management framework is implemented in the industries to identify the fault type, assess fault severity, and predict the future state or remaining useful life to optimize the maintenance activities. Three significant aspects of a prognostics and health management framework are diagnostics, prognostics, and decision making. This article presents a review of different types of diagnostic and prognostic approaches (i.e. physics-based, data-driven, and hybrid approaches) developed for the gears. The flow of information between diagnostics and prognostics parts of the framework is briefly discussed. Regarding the physics-based approaches, this article discusses different physics-based diagnostic and prognostic models developed for different types of gear failure modes such as crack, pitting, and wear. In the data-driven approaches, the article attempts to summarize the data processing techniques used for extracting fault-related information from the recorded raw vibration signal, health indicators developed for different kinds of gear failure modes, processing/selection approaches for best health indicators, fault classification, and fault prognostic models particularly developed for the gear. The article discusses how a hybrid approach can be developed by the integration of a data-driven diagnostics approach and a physics-based prognostics approach. Finally, uncertainty quantification of prognostic approaches, performance evaluation metrics, decision-making strategies, and future research and development perspectives are discussed. This article focuses on the diagnostic and prognostic approaches developed for gears, given the fact that these approaches for other components such as bearing and batteries are reviewed in the past.

预测和健康管理已成为现代行业组件生命周期的重要组成部分。在行业中实施了预测和健康管理框架，以识别故障类型，评估故障严重性并预测未来状态或剩余使用寿命以优化维护活动。预测和健康管理框架的三个重要方面是诊断，预测和决策。本文介绍了针对齿轮开发的不同类型的诊断和预测方法（即基于物理的，数据驱动的和混合方法）的综述。简要讨论了框架的诊断和预测部分之间的信息流。关于基于物理学的方法，本文讨论了针对不同类型的齿轮故障模式（如裂纹，点蚀和磨损）开发的基于物理的不同诊断和预测模型。在数据驱动方法中，本文尝试总结用于从记录的原始振动信号中提取故障相关信息的数据处理技术，针对各种齿轮故障模式开发的健康指标，用于最佳健康指标的处理/选择方法，故障分类，以及为齿轮专门开发的故障预测模型。本文讨论了如何通过集成数据驱动的诊断方法和基于物理的预测方法来开发混合方法。最后，对预测方法的不确定性量化，绩效评估指标，决策策略，讨论了未来的研发前景。鉴于过去对齿轮，轴承和电池等其他部件的这些方法进行了回顾，因此本文重点介绍了为齿轮开发的诊断和预后方法。