maskindrift

Maskindrift, literally “machine drift,” is a term used in robotics and automated manufacturing to describe a gradual deviation between a machine’s actual output and its expected output or model prediction. The drift can affect position, force, speed, or other process variables and may accumulate over time even when a system appears to function normally.

Causes of maskindrift are varied. They include sensor calibration drift due to aging, temperature changes, and lubrication or wear in mechanical joints; actuator nonlinearity and dead zones; load variation; software model updates or drift in control parameters; and external disturbances such as vibrations. Drift can be systematic, presenting as a bias in measurements, or stochastic, appearing as random fluctuations.

Detection and measurement typically rely on comparing observed behavior with a reference trajectory or model. Methods include calibration checks, sensor fusion residual analysis, change-point detection, and statistical process control. In complex systems, drift may be monitored via health diagnostics and anomaly detection to trigger maintenance before performance degrades significantly.

Mitigation strategies focus on maintaining accuracy and stability. Approaches include regular recalibration and environmental compensation, adaptive control or model updating, redundancy and fault-tolerant design, and predictive maintenance to replace worn components. Robust control techniques can reduce sensitivity to drift, and ongoing system identification can keep models aligned with current hardware.

Impact: unaddressed maskindrift reduces accuracy and repeatability, increases waste and downtime, and can pose safety risks in high-precision or autonomous applications.

See also: concept drift in machine learning; sensor drift; calibration; adaptive control.