Traditional fluid analysis relies on pre-set flagging limits to evaluate the severity of a sample. These limits, refined over time through statistical analysis, provide a baseline for assessing whether test results indicate any maintenance needs.
If a sample falls within acceptable limits, it’s a green light to continue operation. If not, the accompanying fluid analysis report offers insights and actionable recommendations to help pinpoint issues.
At POLARIS Laboratories®, we’ve accumulated decades of data from millions of tested samples, building a massive database that includes lubricant type, grade, machine hours, filter type, equipment and component details, and more.
Using 25 years of historical data, we’ve developed flagging limits based on customer submissions and Original Equipment Manufacturers (OEM) guidelines. These robust thresholds provide reliable guidance for equipment maintenance.
Imagine a system that uses this wealth of data to forecast how equipment health will progress and change over time, moving customers from reactive to proactive maintenance.
But what if we could take this a step further? Imagine a system that uses this wealth of data to evaluate the current state of equipment health and forecast how it will progress and change over time to provide predictive maintenance far in advance.
The system is programmed to learn and improve itself over time as it processes more information. This means moving fluid analysis customers from reactive to proactive maintenance, from diagnostic analysis to predictive analysis.
As data science technologies advance POLARIS Laboratories® is at the forefront reshaping the future of fluid analysis using Artificial Intelligence (AI) and Machine Learning (ML). Our team has just launched a new, robust, and powerful analysis engine that calculates and identifies indicators to generate a readable technical statement, resulting in a streamlined approach to understanding and acting on the information.
By leveraging our vast data sets, we can customize and fine-tune flagging limits and continually adapt as new data from equipment and lubricants becomes available. This evolving analysis provides even more precise recommendations, creating a dynamic feedback loop where the system “learns” from patterns in component, equipment, and lubricant data along with historical results.
This engine represents the advanced capabilities of fluid analysis, combining data analytics with technology to drive reliability and improve maintenance recommendations, and it’s here. Introducing: Aurora.
Aurora is rolling out across our laboratory network, continuously learning in the background to refine its predictions.
Customers won’t see changes on their reports right away, but rest assured, the system is advancing in the background to enhance future recommendations.
As we continue to develop, improve, and perfect this technology, we’re trailblazing the fluid analysis industry in an effort to provide the most accurate, reliable, and timely results for customers so they can save more of their equipment.
