The greatest threat to success in production is unplanned downtime or, in a worst-case scenario, unforeseen catastrophic failure of an asset. It is for this reason there has been a very evident and aggressive evolution toward the prevention of such circumstances. Asset maintenance is, of course, the preventive process being referred to. The accepted methodology for a comprehensive maintenance program has shifted as time has gone by. The first iteration of maintenance was, you guessed it, one of a reactive nature. Something failed so the maintenance cycle was focused on simply fixing, patching or replacing the stripped or blown-out component.
It didn’t take very long to determine that this approach is incredibly inefficient, as well as costly. Since this methodology was eventually determined to be unsustainable, operators started with a new approach - scheduled or routine maintenance. Certainly, a step in the right direction when comparing it to the last methodology, but still very far from optimized. Even where schedules for maintenance are not arbitrarily defined, they’re made arbitrary by the lack of real-time data and insight regarding the current state of an asset. Under this methodology, the operator still has little understanding of when maintenance should be optimally scheduled and runs the highly probable risk they will improperly target a timeframe for maintenance, that’s either too late or too early - each with their own consequences. From this approach, industry began to favor conditional maintenance.
This particular methodology is interesting because it represents a shift toward consideration for the condition of the asset itself. Maintenance would take place after a standardized parameter. For example, every 10,000 rotations of a cylinder or every 20,000 cuts of a saw blade. The right mindset is now there, but without the technology necessary to gauge real-time conditions of the asset, the parameters for maintenance are once again rendered arbitrary. It’s not until the operator has predictive data capabilities at their disposal that they can enact a truly holistic and next level approach to conditional-based maintenance.
This “predictive maintenance” allows engineers to learn from current processes and what the equipment is telling them to confidently build a strong understanding of asset reliability - informing them on when the optimal time for maintenance is.
Without a predictive solution that cuts through the informational overload, operators may not be able to identify the key signals that inform their decisions on maintenance. A Predictive Operations Platform is not a full-suite maintenance solution. However, it can meaningfully support your efforts in optimizing your maintenance program.
To optimize your maintenance efforts with TwinThread, get started today.
