Automakers use AI-driven predictive maintenance and computer vision control to achieve a 35-50% reduction in unscheduled ...

MaintainX reports a rise in predictive maintenance adoption and AI usage, though challenges like aging equipment and cost ...

An innovative software program, coupled with an experienced team of engineers, allows plant operators to evaluate creep and fatigue stress in components exposed to more frequent cycling, thereby ...

In AIoT-based systems, sensors continuously collect high-frequency data such as vibration, temperature, pressure, and electrical signals. These data streams are processed by machine learning and deep ...

Find out what the major mechanical, electrical, and user causes of equipment failure are and what types of equipment are more likely to fail. Why reactive maintenance can be so costly. Conducting ...

AI agents for predictive maintenance in energy infrastructure help cut downtime and boost efficiency. Discover how to design ...

As aging infrastructure and rising costs challenge traditional methods, IoT and smart technologies are paving the way for ...

This article covers some of the leading asset management strategies that help drive efficiencies across plant operations and ...

The journey towards autonomous operations involves incremental steps, each bringing businesses closer to a state where systems can independently manage and optimize processes, ensuring sustained ...

TotalEnergies' deployment of machine learning at its Port Arthur, Texas, refinery demonstrates how predictive AI can ...

Predicting exactly how and when a process tool is going to fail is a complex task, but it’s getting a tad easier with the rollout of smart sensors, standard interfaces, and advanced data analytics.

Members pictured from left to right. The concept of predictive maintenance is familiar to anyone who has owned a car—you regularly have the vehicle inspected, change fluids, replace tires, change ...