Data science for Asset Management

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.

By developing smart algorithms, we help organizations to:

  • Get a better insight into asset conditions
  • Organize maintenance more efficiently
  • Predict risk of failure
  • Reduce maintenance workload

Predictive maintenance

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.

Since 2012, we have been helping these types of companies to:

  • Create a complete view of the condition of their assets
  • Obtain a better understanding of possible failures
  • Extend the lifetime of assets

Gray box modeling approach

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.

Kapernikov has all the required experts in house to make this approach work:

  • Engineers with expert knowledge on machine learning and AI
  • Domain experts with an expertise in asset management

Used technologies:

Python
R
OpenCV

Keras
TensorFlow