Predictive maintenance of can making machine—or PdM, for short—is a method for anticipating maintenance requirements in machines on a factory floor. By analyzing operational data from the machines, patterns emerge that will allow operators to predict when maintenance will be required on any given unit, allowing for it to be planned during less costly times.

In the past, manufacturers would rely on reactive maintenance, otherwise known as the ‘if it ain’t broke, don’t fix it’ method. You can well imagine that servicing machines only when they broke down was a huge cost, both in terms of unplanned downtime and the potential impact to other parts of the machine, as well as the quality of the output for the time that the part was failing.

Predictive maintenance relies on specific information pulled from each machine, to detect potential problems. An example would be vibration analysis. A model that uses a baseline of collected performance data for a machine will be able to detect changes, such as an increase in vibration in a specific part, which could be caused by damage or the introduction of a foreign object. Deviations from the baseline allow operators to predict a need for maintenance before the problem becomes serious, resulting in equipment failure.

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