Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance

Abstract

Predictive maintenance (PdM) has emerged as a vital strategy for minimizing equipment downtime, optimizing operational efficiency, and reducing maintenance costs in industrial environments. However, traditional PdM approaches often fall short in scalability, automation, and real-time responsiveness. This paper proposes a sustainable and scalable model that integrates Robotic Process Automation (RPA) with Machine Learning (ML) to enhance failure prediction and streamline maintenance workflows. The purpose of this integration is to combine ML’s predictive accuracy with RPA’s automation capabilities, enabling end-to-end intelligent maintenance operations that require minimal human intervention.

The methodology involves developing a modular architecture where ML algorithms analyze sensor and operational data to predict equipment failures, and RPA bots automatically initiate preventive actions—such as triggering maintenance alerts, generating service tickets, or updating enterprise systems. The model is validated through a simulation-based case study using industrial equipment datasets, assessing performance in terms of prediction accuracy, response time, and process efficiency.

Key findings indicate that the integrated RPA-ML model not only improves failure prediction accuracy by up to 20% compared to traditional methods but also reduces response time for corrective actions by 35%. Furthermore, the model supports sustainable maintenance practices by minimizing resource waste, extending equipment lifespan, and lowering energy consumption. This research contributes to the evolving field of intelligent maintenance by offering a reusable, scalable, and environmentally conscious solution for modern industrial operations.