A New Methodology for Estimation of Dynamic Remaining Useful Life: A Case Study of Conveyor Chains in the Automotive Industry

In the context of industry 4.0 and digital transformation, the predictive maintenance (PdM) approach plays an important role in the efficiency of maintenance, as breakdowns and over-maintenance. Remaining Useful Life (RUL) is the main part of the prognostic aspect of maintenance. RUL is also one key piece of information that feeds the PdM and needs to be dynamically readjusted in a global approach and procedure. In this paper, a new methodology for the prognostic aspect of PdM, and a dynamic RUL estimation method have been proposed. In this way, general methodologies and processes of prognostics have been presented that outline a more coherent vision toward RUL estimation. Within the dynamic RUL method, it is proposed to use from Prophet prediction model in a dynamic algorithm to better estimation of RUL based on the Health Indicator (HI) trends updates. The applicability and efficiency of the proposed procedure and method have been applied and validated in the conveyor chains of an automotive company which their failure results in production stoppages and significant damages. The performance of the prediction method has been presented with a comparison to deep learning and statistical prediction methods. Following the dynamic RUL estimation, a maintenance strategy has been proposed for the studied case to improve maintenance planning.