Advancing Predictive Maintenance and Asset Management Through Digital Twin Technology: A Step Towards Industry 4.0

Digital Twins (DTs) are revolutionizing predictive maintenance (PdM) and asset management by creating a seamless link between physical and virtual systems. This study explores the use of DTs in integrating IoT sensor networks with advanced machine learning models such as Random Forest Classifiers, Long Short-Term Memory (LSTM) networks, and autoencoders. These technologies give meaningful insights into asset performance by enabling predictive analysis and real-time monitoring. The constraints of traditional maintenance techniques, such as delayed fault detection and poor resource allocation, have been addressed by DTs, which have shown notable benefits, such as a 20% increase in asset utilization and a 25% decrease in unscheduled downtime. Longer equipment lifespans and more economical maintenance techniques result from these devel-opments. Notwithstanding its potential, scalability and system integration issues still exist. DTs' critical role in improving system dependability, operational efficiency, and economic viability, all of which propel industrial advancement in the context of Industry 4.0, is highlighted in this paper.