A new hybrid data and model-centric predictive approach dedicated to industrial pipe maintenance

Abstract

Predictive Maintenance (PdM) for pipe clogging is a critical challenge in the industrial sector, particularly with the increasing adoption of Artificial Intelligence (AI) and the Internet of Things (IoT). Frequent clogging incidents, such as those faced by Orano/La Hague, lead to energy waste, operational inefficiencies, financial losses, and potential safety hazards, highlighting the critical need for effective maintenance solutions to protect both assets and personnel. This study proposes a novel hybrid approach that combines the strengths of data-centric and model-centric methodologies for Prognostic and Health Monitoring (PHM) of pipeline systems in constrained industrial environments. The approach utilizes passive acceleration measurements to predict clogging occurrences and quantify clogging severity under varying airflow rates. Experimental results indicate that the proposed method achieves up to 100% accuracy in clogging detection and robust performance across diverse operational conditions. This integrated methodology represents a significant step forward in predictive maintenance, offering scalable and adaptable solutions to enhance safety, operational efficiency, and cost-effectiveness in industrial settings.