Predictive Maintenance

PREDICTIVE MAINTENANCE IS A DECISION-MAKING FRAMEWORK.

Predictive maintenance can be defined as planning and executing maintenance operations based on need, rather than on a fixed schedule. With our technologies for predictive maintenance we can detect and prevent failures in advance to improve performance in terms of finance, safety and legal regulations. Our framework answers to the question “what and when should be done“ instead of “how it should be done”.

We have created technology solutions that enable failure detection to happen either fully automatically or by human aided by meaningful analytics. In many applications the need for maintenance can be predicted and prioritized by advanced AI algorithms with little or no human interaction.

Although fully automatic decision processes are something to desire for and increasing in number, usually at least the final conclusions are drawn by a human. In some cases that is even mandatory for regulative and/or security reasons.

“If we can save a single major component, the predictive maintenance solution has paid off the license fees for its lifetime.”

This quote is from a customer to whom our team was developing a software solution for predictive maintenance. It reveals the size of the business potential around preventive service actions.

TOP-NOTCH ML EXPERTISE.

We help businesses to build their predictive maintenance capabilities and tools. We know that all pieces of the chain need to be involved to create continuous practices around predictive maintenance.

Our predictive maintenance expertise includes e.g. ensuring data quality for its particular use, building predictive machine learning models for individual failure mechanisms, identifying data flaws that compromise the integrity of the results, and optimizing the overall data-to-value processes.

BENEFITS OF PREDICTIVE MAINTENANCE.

  • Improved safety
  • Maintenance process automation
  • Spare parts inventory planning and forecasting
  • Increased productivity
  • Realtime monitoring of the equipment fleet
  • Reduced downtime
  • Cost savings

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