PLSCO: An Optimization-Driven Approach for Enhancing Predictive Maintenance Accuracy in Intelligent Manufacturing

Abstract

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments.