Adaptive Ensemble Learning for Predictive Maintenance: Neural-Gated Mixture of Experts Architecture

Predictive maintenance has emerged as a critical technology in Industry 4.0, yet existing solutions often rely on single models that may not capture the full complexity of industrial failure patterns across different operational conditions. This paper presents a Mixture of Experts (MoE) architecture that leverages neural network-based dynamic gating to intelligently combine predictions from diverse machine learning models. Our approach employs four expert modelsRandom Forest, K-Nearest Neighbors, Gradient Boosting and XGBoost, -coordinated by a neural gating network that learns to assign weights based on operational conditions. The gating network consists of a four-layer architecture with dropout regularization, enabling adaptive expert selection for different failure patterns. Evaluated on a synthetic industrial dataset with 10,000 instances, our MoE system achieves 99.34% accuracy and 0.994 AUC, surpassing the best individual model (XGBoost at 98.52%). This work establishes a foundation for intelligent ensemble methods in predictive maintenance, with future extensions planned for multi-class failure mode identification and temporal pattern integration.