Getting Data Science to Deliver Results for You using ei3’s Proven Methodology
Predicting the health of your machinery is much harder than asking for a limit set of predetermined convenience functions and requires specialized algorithms that are tuned to the machine and its operating environment. The best AI tools are needed to create successful predictive algorithms, but most of all talented and experienced data scientists that understand them, as much as the engineering aspects of your machine.
At ei3, we, of course, have a growing toolset of cutting edge algorithms based on machine learning and AI techniques; but we have more. Over years of experience delivering data science projects to our industrial clients, we have developed a systematic approach towards analyzing customer data and developing the necessary insights to create working predictive solutions. This proven methodology reduces risk, time, and cost, and ensures our data science projects deliver outcomes, and positive ROI, for our clients.
Our data science methodology is based on three steps:
- We analyze available data to create a mathematical model of the machine or process we wish to make predictions over. Using our tools, this model – commonly often called “digital twin”, can be created in a semi-automatic fashion based on historic data from the machine itself, or engineering insight into its operations.
- Deviations from that model are analyzed and enriched using semantic information; this process is often called “tagging”. Based on this exercise which combines analytics with the engineering insights into your machine, deviations can be interpreted in terms of machine anomalies, impending failures, or wear-and-tear events.
- Finally, model and tags are translated into a predictive algorithm that is run continuously on real-time data, creating alerts to the right users, or events in a back-end system, as appropriate to any detected data anomaly.
Siri – Alexa – and all the others – listen up! Our system can give engineers and operators the answers they are really looking for: advance warning when things go wrong.
Interested in hearing outcomes from the application of AI algorithms in real production environments?