Tagup applies machine learning research from MIT to proactively predict equipment failures, mitigating public safety incidents, unplanned outages, and lost revenue.
Tagup models are derived from the world’s largest industrial equipment datasets, providing customers better insight into asset health and an enhanced decision-making capability.
Employing machine learning methods to industrial operations require very large data sets, such that the larger the data set, the better the model.
Not only are Tagup models based on the largest industrial datasets of their kind, but they are dynamically evolving over time as a function of asset types and operating conditions.
Cross industry learnings and their resulting failure analyses are captured in the models as they evolve, benefiting customers in real time.
The process is highly automated so that customers gain the most value from their operating assets.
Effectively deploying the Tagup Suite first requires a comprehensive understanding of your asset health.
Asset value is driven principally by reducing the probability of failure through early identification of developing failure modes. Instead of waiting to respond to a costly failure after it happened, appropriate maintenance actions can be scheduled and taken during targeted preventative maintenance planning (i.e. using Tagup solutions to predict which units will fail and when).
By reducing failure probabilities, facilities recognize value through two primary channels: avoided replacement costs, and avoided lost revenue.
As such, the RAP is designed to take a deep dive into your asset inventory and operating performance, and develop a basic proof of concept (PoC) plan that is scalable to larger asset classes.
The RAP trains customers through this process with the necessary tools to expand the scope to varying asset types while accurately deploying the Tagup Suite.