The rise of 24/7 asset monitoring and the internet of things (IoT) have produced many benefits, but they’ve also introduced complexity. Much of this is due to the proliferation of monitoring data derived from the sensors you use to instrument your equipment. Potential advantages only become actual benefits if you can build a usable methodology that applies 24/7 data in smart ways that yield meaningful operational improvement.
To do this, you need a platform that not only gathers the data from the sensors, but applies basic logic to it in critical ways:
And so on. It’s the kind of logic that human operators would apply anyway when they arrive to diagnose a problematic situation. But with the right information about the site, computers can use machine learning to do it faster, more proactively, and at a much deeper level than human observation traditionally allows.
By overlaying detailed schematics about each system and its sensors with known facts about the physics of how each element should behave, you can build toolsets that go a long way to automatically discovering design faults, detecting potentially miscalibrated sensors, and proposing corrective action.
At Tignis, we help you streamline and modernize the ways you collect, analyze, correlate, and report sensor data in your environment. Rather than simply having data for data’s sake, we get you to a place where the data you’re collecting has direct impact on your operations, enabling the analysis you need to achieve real business outcomes.