Machine downtime, by and large, is a random event, which explains why failure prediction remains an elusive goal in food manufacturing.
Predictive maintenance certainly is possible, as practices in other industries demonstrate. The big obstacle is cost: Establishing an in-house predictive maintenance program “can easily exceed $100,000 for start-up alone,” according to authorities at SKF Inc., Lansdale, Pa. It also requires another precious commodity: time. Time-based maintenance routines are not a panacea for random breakdowns, but they are evidence of a plan to reduce their frequency.
The cost of infrared thermometers, ultrasonic probes, vibration monitors and other tools of condition monitoring are declining, although price points still may exceed the budgets of smaller food and beverage manufacturers. Smart sensors that provide similar feedback may make affordability a non-issue, but if they simply generate more data, they won’t move the industry any closer to predictive maintenance.
Predicting failure for a specific machine may be a challenge beyond the scope of vibration analyzers and temperature probes. A broad sample is needed for reliable prediction. Equipment manufacturers theoretically could determine the early warning signs of a breakdown, but OEMs seldom are able to benchmark performance once machines leave their shops. That’s beginning to change.