Condition-based maintenance in hydroelectric plants: A systematic literature review

Abstract

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.

A Data-Driven Framework for Small Hydroelectric Plant Prognosis Using Tsfresh and Machine Learning Survival Models

“…Reports on the prognoses of hydroelectric generating units are scarcer than publications related to their diagnosis [ 23 ]. A great challenge in the area is proposing procedures that contemplate faults between different generating units and auxiliary interconnected systems [ 23 ].…”

“…Reports on the prognoses of hydroelectric generating units are scarcer than publications related to their diagnosis [ 23 ]. A great challenge in the area is proposing procedures that contemplate faults between different generating units and auxiliary interconnected systems [ 23 ]. An et al presented a prognosis model based on the application of Shepard’s interpolation of three variables: bearing vibration, apparent power, and working head [ 24 ].…”