Predictive maintenance is designed to help determine the condition of equipment in order to estimate when maintenance should be performed. This approach promises cost savings, because tasks are performed only when warranted. According to data from McKinsey, predictive maintenance tools can reduce manufacturing machine downtime by 30 to 50% and increase machine life by 20 to 40%.
With AI-powered visual search and Clarifai’s deep learning algorithm monitor the performance and condition of equipment to reduce the likelihood of failures, lengthen inspection times, and increase the life of capital assets.
Manufacturers can implement safeguards that notify the right people when a piece of equipment needs to be inspected. Build AI models to anticipate the likelihood of a potential breakdown before it occurs. Identify which equipment is at greatest risk of failing, allowing maintenance teams to respond accordingly.
Supply Chain Operators can build models to inform the team how long an asset, system, or component could be offline, allowing them to plan accordingly.
OEMs can collect data from customers’ equipment and build models to provide their customers with insights and equipment-specific maintenance schedules.
Public sector agencies can use computer vision and machine learning to monitor operations more efficiently, and better manage supply chain operations. See when new parts and overhauls will be required, keeping expensive assets, like helicopters, aircraft, and weapons systems in use longer. Address time-consuming FMECAs by running models that can predict patterns based on different asset environments.
Get early warning notifications ahead of potential problems.
Stop machines from failing and avoid unplanned downtime.
Reduce unscheduled repairs during off hours and in remote locations.
Get a better handle of asset replacement planning and delay CapEx.
Lengthen time between physical inspections.
Contribute to employee safety and business continuity.
Improving the customer experience with Snap and Search.
Connecting customers to the products that match their style.
Using AI on mobile devices for disease diagnosis.
Improving search results of user generated images.
Providing enhanced automative images using computer vision modeling.