A Hybrid AI Approach for Predictive Maintenance in Manufacturing Using Machine Learning and Digital Twin-Based Failure Forecasting

Predictive maintenance is a strategy that has gained critical role in the contemporary manufacturing process because of the growing need to achieve greater equipment availability, lower unplanned downtime, and minimize cost of maintenance.The conventional maintenance procedures, including reactive and timebased preventive maintenance, tend to be ineffective in identifying early trend of degradation and result in unneeded component replacement or unexpected machine failure.This paper aims to solve these issues by presenting a hybrid artificial intelligence (AI) method to predictive maintenance by combining machine learning (ML)-based predictive maintenance and digital twin-based predictive maintenance.The suggested framework will integrate real-time condition monitoring information obtained by industrial sensors (vibration, temperature, current, and acoustic information) with sophisticated preprocessing and feature extraction to produce strong health indicators.ML models are used to determine abnormal operating behavior, forecast failure risk, and determine Remaining Useful Life (RUL) of important components.Simultaneously, a digital twin module constructs a virtual model of the physical object to model the conditions of its operation, the processes of degradation and failure evolution under the various loads.The hybrid integration makes it possible to do two-layer validation, i.e., machine learning predictions are checked in the digital twin simulation and thus they minimize false alarms and enhance the reliability of decisions.Also, the digital twin facilitates the what-if analysis of maintenance scheduling and resource scheduling.