Improving asset management of the Belgian electrical grid with a data-driven approach
Kapernikov helped Elia to obtain an accurate overview of the condition of all its network equip ...
Operational Intelligence
Improve the reliability of your assets with data
Kapernikov helps manufacturers and utility companies
make the most out of their asset data with data science.
Predictive asset analytics enables companies to deploy resources more efficiently, lower maintenance costs, improve uptime, and make smarter decisions related to maintenance and asset lifetime in general. Predictive maintenance programs rely on smart use of data and information from a wide variety of measurements.
Kapernikov specifically focuses on the data needs of utility companies and manufacturing companies.
The number of equipment failures in many utility or manufacturing environments is often quite low. As a result, there is not always much data about failures to work with in a classic machine learning approach. Kapernikov therefore follows a gray box strategy, whereby predictions of failures are based on a combination of mathematical models on the one hand and machine learning techniques on the other hand.