AI Driven Machine Health Monitoring and Diagnostics

Predictive maintenance strategies powered by artificial intelligence are transforming industrial equipment monitoring practices. This paper presents an integrated AI -driven approach for health monitoring and diagnostics of HVAC systems using machine learning techniques. We develop models that estimate Remaining Useful Life (RUL) of equipment components using a dataset comprising operational cycles, settings, and sensor measurements. Our framework incorporates anomaly detection to identify irregular patterns indicating potential faults, an interactive chatbot interface for maintenance personnel to query system status, and a dynamic feedback mechanism that continuously improves prediction accuracy. Experimental results demonstrate that our approach achieves significant improvements in maintenance planning while reducing operational costs and unexpected failures. This comprehensive solution represents an advancement in predictive maintenance technology with applications across various industrial sectors.