Together we bridge the gap between patterns in machine data and your own technical expertise. This way, you’ll always have accurate insights in the condition of your machines.
Generate data patterns and store these insights in a digital knowledge base.
Add specific knowledge and context to the generated patterns.
Move from insights to real actions and take appropriate measures to maximise OEE.
Use different data sources and machine learning methods to detect patterns you didn’t know exist.
Your experts view all labelled patterns and manage the machine knowledge base accordingly.
Root cause analysis is used to interpret machine data, either streaming or batch. Conclusions are integrated in your CMMS system.
When an unknown data pattern is detected by our algorithms, the maintenance engineer labels the pattern and stores it in the digital knowledge base.
From now on, the root cause analysis automatically interprets the right cause the next time a known pattern is recognised. This way the knowledge base matures over time and you know how to diagnose more machine faults after each iteration.
Our root cause analysis is powerful alone, but it’s more valuable in combination with our fault diagnosis, fault prediction and service planning algorithms.
Monitor your assets in real-time to identify a machine fault – either performance or health related – and determine the level of severity.
Determining when a condition leads to a critical machine failure and calculate the remaining useful life of the machine.
Act on predictions and prioritise automatically generated service orders in the CMMS based on the potential impact of machine OEE.
Plan the right maintenance engineer for the right service order. Know exactly who has to be where and do what.