A NEW METHOD FOR PREDICTING THE REMAINING EQUIPMENT LIFE FOR HIGH-FREQUENCY DATA WITH NON-UNIFORM DUTY CYCLES

Abstract

Advances in mechanical engineering make it possible to create more advanced and efficient equipment, but at the same time, its complexity and the requirements for managing its life cycle and maintenance increase. Requirements for reliability and availability also create additional challenges to life cycle management. There are various maintenance planning strategies. Among them, the most promising is the predictive strategy based on forecasting the remaining useful life of the equipment. Existing methods for predicting the remaining useful life of equipment focus on the use of historical data aggregated by work cycles, while there are no widely used methods for forecasting using continuous data, including high-frequency data, received from equipment and containing work cycles of various durations and data recorded during downtime. To solve this problem, a method for predicting the remaining useful life is proposed with the determination of work cycles in the initial data and the aggregation of their values into one-dimensional vectors for the purpose of further use for training the forecasting model. The results demonstrate the successful applicability of the proposed method - in combination with the XGBoost forecast model, it is possible to achieve accuracy on data obtained from a gas turbine engine with a root mean square error of 14.02 and mean average error of 10.71.