The collection focuses on predictive maintenance methods leveraging AI and machine learning technologies across various industries, including manufacturing, automotive, and telecommunications. Topics include deep learning models for fault detection, condition monitoring techniques, and real-time data analytics. Emphasis is placed on enhancing operational efficiency, reducing downtime, and optimizing maintenance schedules through data-driven insights and predictive analytics. Case studies highlight practical applications that demonstrate the effectiveness of these approaches in improving machinery reliability and performance.
The Hidden Cost of Bad Spare Parts Planning (And How AI CMMS Fixes It)
