AI-Driven Predictive Maintenance Framework for Smart Engineering Systems Using Multivariate Sensor Data

Abstract

This study presents an AI-Driven Predictive Maintenance (AI-PdM) framework that fuses multivariate sensor streams (vibration, temperature, pressure, current draw, and lubricant condition) with deep learning-based prognostic models to forecast impending failures and estimate Remaining Useful Life (RUL). The study integrates data preprocessing, statistical and spectral feature engineering, SHAP-based feature selection, and an ensemble of Long Short-Term Memory (LSTM), Transformer, and one-dimensional Convolutional Neural Network (1D-CNN) architectures benchmarked against Random Forest and Support Vector Machine baselines. The framework was evaluated on the NASA C-MAPSS turbofan degradation dataset, the PHM 2012 bearing dataset, the SECOM semiconductor manufacturing dataset, and real vibration-temperature-pressure sensor logs collected from three engineering assets: a CNC machining center, an HVAC chiller unit, and a wind turbine gearbox. Experimental results show that the AI-PdM framework achieves a failure-prediction accuracy exceeding 95% (92-98% range across models and horizons), with an average early-warning lead time of approximately 60 hours (range 48-168 hours) ahead of actual failure events. RUL estimation attained a Mean Absolute Error (MAE) of 10-15 cycles and a Root Mean Squared Error (RMSE) of 12-20 cycles. The framework maintained a false-positive rate at or below 3%, cutting false alarms by roughly 60% relative to conventional threshold-based monitoring, and remained robust to sensor degradation, with accuracy declining by less than 5% under 20% missing data or 10% injected noise. Deployment simulations across the three case-study assets demonstrate that the framework generalizes with greater than 90% accuracy, reduces unplanned downtime by approximately 60% (range 50-70%), and lowers overall maintenance cost by approximately 35% (range 25-40%) relative to reactive and preventive strategies.