Data-Driven Insights on Time-to-Failure of Electromechanical Manufacturing Devices: A Procedure and Case Study

Abstract

Nowadays, there is a fresh push towards putting more attention on sustainability issues without affecting productivity as main target in Industrial Cyber-Physical Systems (ICPS). In this direction, this work proposes a procedure and presents a data-driven insight method in order to predict the remaining useful life (RUL) and to classify faults by a condition base-monitoring (CbM). Therefore, by using a framework that combines both outputs, a maintenance stop can be scheduled near to the failure, thus improving its sustainability, without affecting productivity. A fuzzy decision-making strategy supported on generated insights is developed in order to extend the useful life of electromechanical devices. A case study is presented in order to assess the proposed methodology using a dataset of bearing faults. Experimental results and its comparison with previous reported works corroborate a good trade-off solution offered by the proposed procedure considering productivity and sustainability for bearing faults detection.