Let the data tell you when to service. Not the calendar.
Predictive maintenance is the most modern of the maintenance strategies and the one most often oversold. The technology has gotten cheaper, vendors are aggressive, and a lot of shops are buying sensor packages that produce dashboards nobody reads. Done right, predictive catches failures forming weeks before they happen and replaces a chunk of preventive work that was over servicing equipment. Done wrong, it is an expensive way to confirm what a quarterly inspection would have caught for free.
"A sensor reading no one looks at is the same as no sensor at all."
Predictive maintenance follows the same loop on every implementation: pick a failure mode, find a signal that precedes it, set a threshold, monitor the signal, and act when the threshold is crossed. The signals fall into a small number of families. Vibration analysis catches bearing wear, misalignment, and imbalance in rotating equipment. Thermography catches electrical hot spots and lubrication issues. Oil analysis catches particulate wear, contamination, and viscosity breakdown. Ultrasonic detection catches air leaks and early bearing wear. Motor current signature analysis catches electrical faults forming in motors and drives.
The threshold is the make or break parameter. Set it too tight and the shop chases noise. Set it too loose and the failure happens before the alarm. Initial thresholds usually come from the equipment manufacturer or the sensor vendor and get refined over the first few months based on actual readings and actual outcomes. A program that does not adjust its thresholds is not really running predictive maintenance; it is just collecting data.
Predictive does not replace preventive maintenance. It augments it. A mature program runs preventive on parts where the calendar is reliable (lubricants, filters, consumables) and predictive on parts where the calendar wastes money (bearings, drives, hydraulics on critical equipment). The maintenance team manages both schedules from the same plan.
Picture a 30 person food packaging shop with two filler lines that run 16 hours a day. The fillers have six gearboxes each. When a gearbox fails it shuts the line for eight to twelve hours and contaminates a batch. Preventive maintenance changes the gearbox oil every 1,000 hours, which is sometimes too soon and sometimes too late. The shop has been losing one unplanned line stop a month, costing roughly $8,000 each.
A modest predictive program would add a vibration sensor and a temperature sensor to each gearbox. Total cost: about $4,000 for sensors and a handheld reader, plus a monthly route that takes an hour. The shop establishes a baseline over six weeks, sets thresholds, and starts catching gearbox wear forming weeks before failure. The first two saves pay for the program. The gearbox oil schedule shifts from calendar based to condition based, which extends interval on the gearboxes that are running clean and shortens it on the ones that are not. The work is now driven by what the equipment is telling the shop.
Predictive maintenance is one mode of planned maintenance and lives alongside preventive maintenance in a mature program. It complements autonomous maintenance by catching what operator inspections cannot see, and feeds the broader total productive maintenance framework. When new equipment is being specified, early equipment management is where predictive sensor points get designed in upfront.
The questions we hear most about this term.
Long-form guides that pick up where this definition leaves off, written for manufacturers running Arda today.
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