Since the beginning of mass manufacturing and automation, machinery has required maintenance in the form of time and material to fix the inevitable breakdowns. In its most basic form, maintenance is reactive and performed on machinery that is already broken. This means unplanned downtime, unpredictable lost production, and ultimately, lost profits.
In the second maintenance iteration, modern factories, vehicle fleets, and even vending machine operations have implemented preventive maintenance programs. In this scenario, potentially downtime-causing wear items are replaced or serviced at regular intervals. While an improvement in terms of scheduling, this also means that machinery being “PM’d” (preventive maintenance-ed) must be out of service more than absolutely necessary.
The third maintenance paradigm, predictive maintenance, takes efficiency one step further. Here the engineer or operator—or even better, the system itself—is able to sense when something is amiss, well before problems arise. This allows maintenance operations to be set up and executed when convenient before a full breakdown of equipment.
This third iteration avoids both excess planned downtime and emergency situations where machinery simply “schedules its own maintenance,” or breaks down. However, true predictive maintenance programs normally take significant work and intelligent planning to set up and execute. So, while the benefits may be evident, it is still not always implemented. But why?
When considering the transition to predictive maintenance, three questions will naturally arise:
- How does one know when machinery needs maintenance without an actual breakdown?
- Is predictive monitoring difficult to set up?
- Does predictive maintenance mean continuous monitoring by an engineer when learning the system, and further monitoring once implemented?
As discussed in this white paper, the answer to all three questions is Clea, a predictive artificial intelligence (AI) solution from SECO Mind, which can be deployed at the edge or in the cloud expediently for a given operation.
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