You can automate the monitoring and analysis of your connected mechanical systems once and for all, with Tignis. We use the principles of physics to drive our analysis in order to deliver better outcomes, enabling you to leapfrog many of the obstacles limiting results today.
Physics-driven mechanical systems analytics utilizing digital twin and machine learning technologies.
Building systems automation (HVAC), process manufacturing, oil and gas, clean rooms and more.
Increased reliability through automated monitoring and analysis of your mechanical systems, continuous anomaly detection, and precise identification of operational impacts.
Tignis enables you to simplify system monitoring processes, filter out the “noise” of false positives, and gain a more durable, digital foundation for understanding and mapping the processes and priorities you care about day-to-day.
You can rely on Tignis because we are proven experts in machine learning, artificial intelligence, physics, cloud and edge software, data security and data management. We have decades of experience building enterprise-grade software and delivering it to the largest companies worldwide.
The deeper dive
Many facilities use networks of sensors to generate data for monitoring and maintaining assets. The resulting data sets are rich fodder for analyzing system faults and optimizing the ways you manage assets in your built environments. It’s tempting to think that the more data you have, the more accurately you can detect, predict and prevent failures.
However, even with all that data, it can be hard to diagnose relevant issues in a timely way. Sensors are an imperfect technology—they can be miscalibrated to send the wrong data, they can send data you don’t really care about, and of course, they can age, weaken and fail. Accounting for the limitations of sensors is an important part of maintaining healthy asset monitoring and management.
Tignis helps you put sensor data to work in helpful, sensible ways by applying physics-based modeling and a digital twin as part of your sensor-based monitoring solution.
The digital twin is a virtual replica that accurately captures the myriad ways each system part relates to and impacts the other parts so that you can develop meaningful knowledge about them.
Tignis’ physics-based modeling applies physical laws to the data models in your digital twin so that our solution can understand and learn usage patterns that conform to physical logic.
Tignis’ algorithms and analytics are capable of delivering highly-accurate initial results and ongoing advantages by: automatically monitoring and learning; continuously detecting threats to reliability, even on diverse and complex systems; and precisely identifying and predicting operational impacts.
Using Tignis’ physics-driven mechanical system analytics, with digital twin and machine learning technologies, you can increase reliability and modernize your long-term asset management approach.