The mechanical systems you monitor play a vital role in your operational success. Technology has progressed in ways that deliver more data and possibilities for increasing the reliability and availability of your systems, but at the end of the day, the challenge remains what it’s always been: Making sure it all works together, efficiently and effectively, with a minimum of downtime or unexpected hassle.
And it’s not just about your company’s success. As an asset manager or other stakeholder in the system reliability, your time, performance, and reputation are also on the line. The smarter you can be about overcoming the most common obstacles to reliability, the better you can succeed and grow in your role.
Tignis provides unique physics-driven analytics that uses digital twin and machine learning technologies to increase the reliability of your connected mechanical assets. Starting with your piping and instrumentation diagrams and the history of sensor data you’ve already collected, we produce a durable, extensible digital replica of your environment and then work with you to develop precise analytics that serve your specific asset characteristics and business needs.
In this way, we help you cut through a lot of the noise that you probably deal with every day, such as false positive readings, wasted time cycles spent chasing down the root cause of an issue, and overall exposed asset risk due to a variety of unknown environmental factors.
By proactively navigating the intricacies of sensor configuration, the proliferation of sensor data, and the ongoing challenges of swift issue identification and resolution, we can help you make better sense of the assets you oversee, and manage them accordingly.
Welcome to our blog. The stories and insights we share here will connect your challenges and obstacles to the best outcomes for transforming the way you see asset management analytics, modernizing and streamlining your approach to reliability and maintenance, and doing your job in even more effective ways than you have before.