Maintelligence: Multi-Sensor Fusion and Explainable AI for Next-Generation Predictive Maintenance in Industrial Systems

Predictive maintenance (PdM) is important for reducing unplanned downtime and improving efficiency in modern industrial systems. Traditional maintenance methods, which are often reactive or based on schedules, do not catch early-stage issues and lack useful insights. This paper presents Maintelligence, a framework that combines multi-sensor fusion, time-series forecasting for Remaining Useful Life (RUL), anomaly detection, and Explainable AI (XAI). We used datasets from NASA CMAPSS and Kaggle predictive maintenance repositories to test the framework. LSTM and GRU models provide precise RUL predictions, while Isolation Forest and Autoencoder models find anomalies. SHAP and Integrated Gradients help explain results, allowing engineers to see the key factors involved. A real-time dashboard shows how the framework can be used in industry. Our experimental results indicate significant improvements in prediction accuracy, early fault detection, and practical decision-making. Maintelligence connects the gap between theoretical research on predictive maintenance and its real-world use.