Cognitive Predictive Maintenance is a tool, that uses machine learning to monitor machine-component performance through networked sensors in real time to detect early warning signs that a machine might be breaking down to prompt preventative maintenance. Servicing machines before they break down is more cost effective than fixing them after they break, and the algorithm can predict when maintenance is needed 300 percent more accurately and 30 times faster than traditional methods.
Manufacturing facilities can include 1,000 networked manufacturing units automatically coordinating with one another to retrieve and fabricate components without human oversight. Such a facility is approximately 75 percent autonomous, allowing its human employees to focus on monitoring the factory floor and operating computer systems.
Networked sensors in subsea oil wells use machine-learning software to monitor oil-well performance in real time, allowing to maximize production and minimize downtime. The software analyzes data such as pressure, machine vibration, and temperature to provide oil-well workers with relevant insights into how to make wells operate more efficiently, reducing operational costs, and how to avoid unexpected failures.