© 2016 Sierra Nevada Corporation   364 slip ring rotor and move onto adjacent circuits.  This hypothesis was confirmed with a successful random  vibration re- test following SADA rework by repositioning  the brushes to their assigned grooves and  increas ing the rotor axial preload to withstand higher -than- expected rotor acceleration responses . 

Several years later, after two successful SADA on- orbit missions , protoflight  vibration testing once again  resulted in shorted slip ring circuits. Because of historical issues with slip ring rotor preload,  initial   discovery efforts focused on the as -built condition of new hardware and found rotor preload to be slightly  underrated.  However, s ubsequent rework to correct  rotor preload did not prevent brush movement during  vibration re-tests. It became evident that underrated rotor preload was not the only  physical cause for the  anomaly and SNC began a rigorous  in-depth investigation to reevaluate other  underlying causes . 

Anomaly Investigation  

Dealing with problems associated with the structural response of complex mechanical systems  can be  extremely challenging , especially when investigation and discovery must be conducted on fully  assembled flight hardware with limited visibility or access to key components. This ‘black box’ scenario once again became the backdrop for another  rigorous investigation into the root cause analysis of the  shorted brushes.    The investigation centered on three key areas including review of design/analysis /build documentation ,  assessment of dynamic responses on various SADA components , and development of test correlated  predictive analytical  tools . First, an extensive review of build documentation found the SADA parts to be  correctly manufactured and assembled to print . Review of various structural analysis reports confirmed  positive load margins. All evidence, additionally supported by rigorous visual inspections during hardware  disassembly, convinced the engineering team that the anomaly did not occur as a r esult of a failed or  incorrectly assembled component. No obvious root cause explanation  was found,  so the team focused on  the second area of interest, dynamic response, which quickly became the primary focus of the  investigation.    The team shifted the investigation  focus to data mining, including  exhaustive efforts to collect , evaluate,  and categorize prior SADA acceptance test data . The effort  began with a rigorous study of  accelerometer  output time history in an attempt to further quantify the frequency and nature of peak input loads.  Data   consisted of several vibration tests and re- tests of similar SADA hardware, test setup,  and vibration inp ut  profiles conducted throughout a ten- year period.    Time history vibration data revealed that unexpected intermittent respons es greater than 6 sigma  were  present during test, including tests that resulted in shorted slip ring circuits.  Figure 7 illustrates an  example of a time history plot  recorded during a prior SADA vibration test . This finding was crucial  to the  investigation because it confirmed that the typical 3-sigma threshold used for design and analysis was  inadequate for the design of this particular component  as tested under the specified vibration spectrum   and related test boundary conditions . Review of this data provided a comprehensive understanding of  vibration test effects on and interaction with the SADA that contributed to the team’s ability to develop a  viable method of predicting dynamic response during future tests .    Discovery of these random peak loads was a crit ical breakthrough in the investigation. It became  immediately clear that prior design efforts did not account for these significantly higher dynamic events .  The persistence of these higher events  in historical data highlighted a need to increase t he previo us  minimum design standard from 3 to 6 sigma or greater  for this particular component and to begin work on  the development of  higher fidelity predictive modeling techniques.   

As the effort to develop predictive modeling techniques began, it became apparent that trying to quantify and predict the dynamic behavior and movement of hundreds of independent brushes reacting against the slip ring rotor would be very challenging. Howev er, the team realized that it was not necessary to fully  understand or predict the complex nonlinear behavior of brush dynamics. Instead, the engineering team