Original article was published by SHAIK SAMEERUDDIN on Artificial Intelligence on Medium
These facts point to the need at any stage of the operation for proper and effective asset performance management. Fortunately, for asset-heavy and asset-dependent companies, the advent of the Industrial Internet of Things ( IIoT) comes at the most important moment. In ways you didn’t think existed, combining IIoT with data analytics will empower your business. You will also begin to see and appreciate how all your assets are interrelated and interdependent, with the opportunity to obtain a comprehensive and clear overview of your asset operations. This allows you to predict interferences and interruptions better, which in turn gives you the ability to intercept and prevent unwanted downtimes and crashes of equipment.
Forecasting and managing needs for asset performance
We all know about schedule equipment and maintenance of assets: preventive maintenance management initially seems to be a fairly automatic mechanism that can be entered, placed on a count loop or dependent on timed use of a calendar. Interestingly, however, only about 18 percent of equipment failures are resolved by this seemingly significant method, while a whopping 82 percent of failures are attributed to random incidents that have no visible trend on the surface.
Thus, while there is value in the success of preventive maintenance, it becomes crystal clear that even a quarter of possible asset failures that can haunt a company are not expected by this method, keeping it running below optimum performance levels. Clearly, once data analytics will come to the forefront to predict and avoid unforeseen asset failures, there is a demand to cover the majority of asset failures.
Predictive Maintenance employed
For this significant method, there is a term: predictive maintenance. The fact that up to 82 percent of traditional asset failures can theoretically catch and stop this makes it a mechanism whose time has clearly arrived.
Predictive maintenance uses condition tracking that aims to provide advance warning of probable or certain failure. This in turn helps the technical staff to alert the maintenance team so that before a malfunction happens, they can plan and execute ad hoc maintenance tasks. It is possible to break down the three most common methods of deploying condition monitoring as follows: