Forecasting of the remaining useful life in conditions of small data sample

In the article a method for forecasting the residual life of equipment using deep learning is proposed. The method is applicable in cases with a small amount of information about data failures, where existing classical methods may not provide the required accuracy. The process of maintaining the equipment in working condition is one of the most important processes in the operation of the equipment. At the same time, the maintenance process often suffers from inefficiency. Therefore, forecasting methods were developed, on the basis of which the concept of proactive maintenance process management was built, which allows optimizing the structure and costs of equipment management throughout the life cycle. However, these methods may show insufficient accuracy if there is not enough data to train them, for example, due to the rarity of equipment failures. To solve this problem, a new prediction method based on deep learning is proposed that can improve the prediction accuracy. In this method, the continuous prediction of the remaining service life over the entire interval is replaced by a model for generating signals containing the calculated prediction.