Conceptual Framework for AI-Driven Predictive Maintenance in Water Purification Infrastructure

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers in predictive maintenance and water infrastructure can use the proposed layered architecture as a basis for empirical studies.

🏢実務担当者:Water utility managers can adopt the framework to reduce emergency repairs and lower lifecycle costs.

🏛政策担当者:Policymakers can consider integrating AI-based maintenance into national water infrastructure strategies to improve resilience and sustainability.

This conceptual review proposes an AI-based predictive maintenance framework for water purification systems in developing regions. The framework combines machine learning, IoT-enabled sensors, and reliability engineering to anticipate equipment faults and schedule maintenance efficiently. Using Reliability-Centered Maintenance and Risk-Based Inspection methodologies, the model helps reduce unplanned downtime, extend system lifespan, and minimize operational costs. This highlights how predictive analytics can transform traditional maintenance practices into data-driven processes, ensuring sustainable access to clean water and improved public health outcomes in resource-limited settings. The proposed framework integrates a layered architecture: an IoT-driven data acquisition layer capturing turbidity, flow, pressure, pH, chemical dosing, and vibration; an edge-enabled preprocessing layer for real-time anomaly detection and data quality assurance; and a cloud-based analytics layer hosting supervised and unsupervised learning models for fault classification and remaining useful life forecasting. Reliability engineering principles guide feature selection and failure-mode prioritization, while Risk-Based Inspection informs resource allocation toward high-consequence components. Decision-support modules translate predictive outputs into actionable maintenance schedules, spare-parts provisioning, and technician tasking that align with local capacity constraints. Implementation considerations for developing regions include low-bandwidth operation, energy-efficient sensors, modular deployments, and workforce training to bridge skill gaps. The framework emphasizes explainable AI models and human-in-the-loop validation to build operator trust and ensure safety. Expected outcomes include measurable reductions in emergency repairs, improved asset availability, lower lifecycle costs, and enhanced water quality continuity. Policy recommendations and partnerships with local utilities, NGOs, and academia are proposed to support scale-up, financing, and long-term sustainability through community-driven monitoring programs. By integrating AI with reliability-centered strategies, the framework advances proactive, resilient, and sustainable maintenance of water purification infrastructure, contributing directly to improved public health and progress toward Sustainable Development Goal 6.

• semanticscholar https://journals.nipes.org/index.php/jete/article/download/3705/2731first seen 2026-07-16 06:22:18