| Summary: | Advancements in energy technologies created a new application for gas turbine generators, which are used to balance load. This usage also brought new challenges for maintenance because of harsh operating conditions that make turbines more susceptible to random failures. At the same time, reliability requirements for energy equipment are high. Reliability-centered maintenance based on forecasting the remaining useful life (RUL) of energy equipment, offers improvements to maintenance scheduling. It requires accurate forecasting methods to be effective. Defining stages in energy equipment operation allows for the improvement of quality of data used for training. At least two stages can be defined: normal operation and degradation process. A new method named Head move—Head move is proposed to robustly identify the degradation process by detecting its starting point. The method is based on two partially overlapping sliding windows moving from the start of operation to the end of life of the energy equipment and Kruskal-Wallis test to compare data within these windows. Using this data separation, a convolutional neural network-based forecasting model is applied for RUL prediction. The results demonstrate that the proposed degradation process identification (DPI) method doubles the accuracy when compared to the same forecasting model but without degradation process identification.
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