Data-model-linked remaining useful life prediction method with small sample data: A case of subsea valve

Abstract

Remaining useful life (RUL) prediction is essential for strategic maintenance planning and enhancing overall operational efficiency. To address the challenges posed by limited data and deteriorated models, this paper introduces an innovative method for RUL prediction using small sample data, leveraging the integration of data and models. This approach utilizes intermittent monitoring data from industrial equipment, capturing extended intervals of small sample data, to establish the equipment's health index (HI). The dataset associated with the HI is expanded to improve model fitting. By converting the HI into reliability metrics, the method tackles data challenges through life analysis based on the Weibull distribution. The interaction between data and the model is used to assess the reliability and RUL of the equipment. The method's application is demonstrated through a case study of a subsea valve. By constructing an RUL prediction framework that integrates data and models and incorporating a parameter uncertainty prediction model, the method provides probabilistic expressions of parameters and results for the entire prediction period, thereby enhancing the accuracy of the prediction results.