Industrial maintenance has become an essential strategic factor for profit and productivity in industrial systems. In the modern industrial context, condition-based maintenance guides the interventions and repairs according to the machine’s health status, calculated from monitoring variables and using statistical and computational techniques. Although several literature reviews address condition-based maintenance, no study discusses the application of these techniques in the hydroelectric sector, a fundamental source of renewable energy. We conducted a systematic literature review of articles published in the area of condition-based maintenance in the last 10 years. This was followed by quantitative and thematic analyses of the most relevant categories that compose the phases of condition-based maintenance. We identified a research trend in the application of machine learning techniques, both in the diagnosis and the prognosis of the generating unit’s assets, being vibration the most frequently discussed monitoring variable. Finally, there is a vast field to be explored regarding the application of statistical models to estimate the useful life, and hybrid models based on physical models and specialists’ knowledge, of turbine-generators.

工业维护已成为工业系统利润和生产力的重要战略因素。在现代工业环境中，基于状态的维护根据机器的健康状况指导干预和维修，根据监测变量计算并使用统计和计算技术。尽管一些文献综述涉及基于状态的维护，但没有研究讨论这些技术在水电部门（可再生能源的基本来源）中的应用。我们对过去 10 年在基于状态的维护领域发表的文章进行了系统的文献回顾。随后是对构成基于状态的维护阶段的最相关类别进行定量和专题分析。我们确定了机器学习技术在发电机组资产的诊断和预测方面的应用研究趋势，振动是最常讨论的监测变量。最后，关于应用统计模型来估计涡轮发电机的使用寿命，以及基于物理模型和专家知识的混合模型，还有一个广阔的领域需要探索。