From Trend Data to Predictive Maintenance for Tongs and Make-Up Equipment
Pressure, speed, torque, action time and repeatability can reveal changing equipment condition before a single alarm does.
Signals worth retaining
Hydraulic pressure and temperature, motor speed, torque deviation, grip build-up time, valve response, cycle count and alarm history form a useful equipment-health baseline.
Trend before point alarms
One excursion may reflect an operating change. Slower actions, higher pressure for equal torque, rising no-load resistance or declining signature repeatability are stronger inspection triggers when they persist.
Turn insight into workflow
A useful system links a trend to the relevant component, inspection step, spare and owner, then records whether performance recovered after maintenance. This closes the maintenance loop.
Build in stages
Start with trustworthy acquisition and visualization, add rule-based alerts, and assess machine learning only when data volume and labels justify it. Predictive maintenance should reduce downtime risk and cost—not maximize algorithm complexity.