I will develop a predictive maintenance system

This project develops a predictive maintenance system using acoustic analytics, advanced soft sensors, and digital twin technology. IoT-enabled sensors and deep learning models monitor and analyze equipment conditions in real-time. Acoustic sensors capture high-frequency sound waves, which are processed with advanced signal processing techniques and machine learning to detect anomalies and predict failures.

Soft sensors provide virtual measurements derived from correlated physical sensors and mathematical models, offering insights into hard-to-measure parameters. Digital twin technology creates a virtual replica of the equipment, enabling real-time monitoring, simulation, and optimization. The system's scalable architecture allows integration with existing infrastructure, enhancing data accuracy and reliability. This solution reduces downtime, improves efficiency, and extends equipment lifespan, ensuring cost-effective maintenance strategies across various industrial applications, from manufacturing to energy sectors.