Unexpected rotating or fixed equipment failure can result in significant production losses, or worse, environmental or human losses for a company. Avoiding this type of failure is therefore at the top of the priority list for Maintenance Managers.

When predictive maintenance is working effectively as a maintenance strategy, maintenance is only performed on machines when it is required. That is, just before failure is likely to occur. However there are several pieces of the puzzle that need to be put in place before there is a fully working predictive maintenance system. Machines, devices, sensors and people need to connect and communicate with one another seamlessly. There needs to be a virtual copy of the physical world in order to make sense of all the data to conceptualize the information. The most sophisticated solution technologies, such as AI, need to be deployed to support decision making and problem solving, making cyber systems as autonomous as possible.

A best in class preventative maintenance and inspection program comes down to intervals. But when is the right time to conduct routine maintenance? Is it when the manufacturer says it’s time, or when the plant says it’s time?

I-sense came up with a whole maintenance solution package for real time asset health monitoring, vibration analysis and predictive maintenance applying machine learning and optimization in order to correct the trajectory and minimize the average downtime of the system.