As discussed in one of our recent posts , the sensors that you use to monitor your facilities can produce a lot of data you don’t really need. Sometimes less is more: You can be more efficient when you’re able to focus just on the data that is sensible and pertinent—even though it’s tempting to think that the more data you have, the better you can perform your duties, and the less susceptible you are to getting blamed for system failures when things go wrong.
But the other important thing to remember is that 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 weaken and fail. Accounting for sensor limitations is an important part of maintaining healthy asset monitoring and management.
Tignis uses the concepts of digital twin and physics-based modeling to create system reliability solutions that put sensor data to work in useful, sensible ways.
- A digital twin is a database that models your installation as a sum of its many components, connections, and characteristics. Without a digital twin, you have no electronic medium for accurately capturing the myriad ways each system part relates to and impacts the other parts so that you can develop meaningful knowledge about them.
- Physics-based modeling applies basic physical laws to the data models in your digital twin so that the solution can understand and learn usage patterns that conform to physical logic.
Using these two elements in tandem has plenty of advantages for modernizing your long-term asset management approach. In a series of upcoming posts, we’ll talk about the three steps you can take with sensors to align with this new perspective on asset management and help improve the ways you monitor system reliability:
- Optimize your use of existing sensors to avoid the unnecessary time and expense of installing new ones.
- Identify faulty sensors and other issues more readily in your environment.
- Eliminate false positives so that your monitoring solution gives you better, root cause-focused reporting.
Stay tuned to learn about each of these steps in more detail.