Where it would be difficult for non-experts to diagnose complex data or where there are multiple assets with multiple data feeds, human decision making can be limited and error prone.
Conventional AI systems base advice upon the identification and verification of outliers. They do not then normally offer a diagnosis of cause, but instead point the user to carry out further investigations and analysis and then update the system with the outcomes, such that if similar events are witnessed then the system can then respond with suggested outcomes. The problem with this is that the system is born naive and requires many learning events to become truly useful.
MIB incorporates the diagnostic knowledge of expert diagnosticians in the conventions of mature condition monitoring techniques and aligns this with AI functionality to identify true outliers and then offer meaningful diagnostic advice.
As more assets are managed remotely and fewer onsite personnel are sufficiently experienced to deal with all scenarios, it is becoming clear that a marriage of technology and experience is needed to balance the equation.
MIB have this as our objective, this is why we exist.
We have the tools to deliver it in a simple software platform that we can place within your existing workflow for maintenance planners in any industry, world-wide.