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David G. Lewicki is a researcher whose documented work addresses gear dynamics, durability and vibration-based detection of tooth damage. His1986 NASA technical paper, Predicted Effect of Dynamic Load on Pitting Fatigue Life for Low-Contact-Ratio Spur Gears by David G. Lewicki, studied how dynamically varying tooth loads change predicted gear surface pitting fatigue life. Its parametric calculations showed why a static transmitted-load estimate alone can miss important speed-dependent effects.^[ref]

The scientific distinction is between the mean transmitted torque and the changing tooth-contact force. Even at the same mean torque, a mesh can have different peak loads as operating speed approaches a structural natural frequency. The1986 study linked these changing dynamic loads to life predictions for low-gear contact ratio spur gears; it was a model-based investigation, rather than a universal measured life law.

Lewicki later coauthored a2015 study with Nicholas A. Lambert and Robert S. Wagoner comparing rotating accelerometers on a helicopter-transmission pinion with stationary housing sensors. That experimental work connects the dynamic-load problem to the practical question of which gear vibration spectrum features reveal damage. The1986 title page gives his affiliation as the Propulsion Directorate at Lewis Research Center; the2015 report lists Glenn Research Center.^[ref]

Nicholas A. Lambert coauthored research on rotating accelerometers for gear damage detection with David G. Lewicki and Robert S. Wagoner. Their2015 NASA report identifies Lambert and Wagoner with Ridgetop Group and Lewicki with Glenn Research Center. The contribution documented there is a joint experimental evaluation of embedded rotating sensing against conventional stationary housing measurements.^[ref]

The apparatus used a microelectromechanical system acceleration-sensing package on the rotating pinion. Its radial channel and two tangential channels did not measure identical combinations of motion. With the reported sensor orientation, adding the tangential channels isolated rotational acceleration, while subtracting them isolated the shared lateral contribution. This makes the experiment an example of choosing measurement geometry to separate motions, rather than interpreting every channel as the same gear vibration spectrum.^[ref]

An idealized algebraic version writes the channels as $T_1=u+r\alpha$ and $T_2=-u+r\alpha$, where $u$ is one projected translational acceleration, $r$ is sensor radius and $\alpha$ is angular acceleration. Then $(T_1+T_2)/(2r)=\alpha$ while $(T_1-T_2)/2=u$. Sensor placement, polarity and matching measurement calibration are what permit those cancellations. This illustrative decomposition explains the measurement principle; it does not assign sole authorship of the apparatus to one coauthor.

Robert S. Wagoner is a coauthor of experimental research using rotating accelerometers to detect damage in transmission gears. In the2015 report with David G. Lewicki and Nicholas A. Lambert, his affiliation is Ridgetop Group. The experiment compared embedded microelectromechanical system sensors with stationary housing sensors while an intentionally notched pinion was run toward tooth failure.^[ref]

A central signal-processing problem was separating repeatable shaft-related motion from unrelated vibration. The study reconstructed a rotation timing signal from a strong periodic component of the measured data and used it for time-synchronous averaging. Its comparison with a measured timing reference tested whether that reconstruction preserved the rotation-related waveform.^[ref]

The underlying averaging idea can be illustrated by records $a_j(\theta )=g(\theta )+{\eta }_j(\theta )$ aligned at the same shaft angle. Averaging $M$ revolutions preserves the repeatable waveform $g$; independent zero-mean noise with common variance ${\sigma }^2$ has mean-record variance ${\sigma }^2/M$. Timing error or changing operating conditions breaks those assumptions. This distinguishes detecting a repeatable vibration feature from proving that it uniquely identifies a tooth defect. The report found that fault indicators generally responded near actual tooth fracture, limiting the demonstrated early-warning interval.^[ref]