Pedro Nunes

A considerable part of enterprises’ total expenses is dedicated to maintenance interventions. Predictive maintenance (PdM) has appeared as a solution to decrease these costs; however, the necessity of end-to-end solutions in deploying predictive models and the fact that these models are often difficult to interpret by maintenance practitioners hinder the adoption of PdM approaches. In this work, we propose a flexible architecture for PdM to recommend maintenance actions. The proposed architecture is based on containerized microservices on intelligent edge devices together with a hybrid model which fuses generalized fault trees (GFTs) and anomaly detection. Results on injection molds carried out at OLI, a Portuguese company, show that the proposed solution is suitable for deploying predictive models and services such as data preprocessing, sensor management, and data flow control, among others. These services run near the shop floor, allowing for greater flexibility, as they may be remotely managed and customized according to the company’s requirements. The results of the GFT model show an estimated reduction of more than 63% in current maintenance costs, while the distribution of analytics tasks by the edge devices reduces the burden on the network, requiring only 0.2% of the current cloud storage.