Hybrid Artificial Intelligence Model for the Transition from Predictive to Prescriptive Maintenance in Textile Manufacturing

Chambi Quiroz N.; Choque Quispe S. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.2536

Resumen

This paper presents the development and validation of a hybrid artificial intelligence framework integrating predictive and prescriptive capabilities to optimize maintenance in the textile industry. The research addresses the high failure rate of critical textile machinery components-such as needles, feeders, guides, and tensioners-whose traditionally reactive management leads to unplanned downtime, increased operational costs, and deterioration of final product quality. The methodology comprises: historical data acquisition, preprocessing, feature engineering (MTBF, MTTR), development of predictive models (ARIMA, XGBoost), and a prescriptive module that transforms predictions into optimized decisions through multi-objective optimization subject to operational constraints. The results demonstrate significant improvements: reduced downtime from unexpected breakages, near-total elimination of fabric waste due to defective seams, decreased sudden failures, increased specific MTBF, and reduction of non-conforming products. It is concluded that the value of predictive maintenance is enhanced when complemented by a prescriptive module that translates predictions into contextualized decisions, bridging the gap between theory and its practical application in real production environments. The framework provides a replicable roadmap for extending the benefits of prescriptive maintenance to the textile sector.

Instituciones: Universidad Nacional de Ingeniería