Using integrated mechanical diagnostics health and usage management system (IMDHUMS) data to predict UH-60L electrical generator condition

Abstract

Military aircraft maintenance methods are moving from practices based on hard-time inspection and replacement intervals to one of Condition Based Maintenance (CBM). Benefits of CBM are the minimization of maintenance efforts and component replacement along with an increase in readiness and safety. Goodrich has developed the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS) for the practices of CBM in helicopters. Great benefits have been realized with the IMD-HUMS system in regards to several maintenance practices, readiness, and safety. However, the total potential of the system in regards to these benefits for the multiple components observed by the IMDHUMS is not yet achieved. The IMD-HUMS gathers a great deal of pertinent, important data on the condition of multiple components and systems, but the meaning and full potential of all this data is not yet fully realized. The purpose of this research is to conduct and document a statistical analysis of IMD-HUMS produced data. Statistical applications of data mining, regression and classification trees are explored. The approaches used in the exploration of the IMD-HUMS acquired data sets are based on six electrical generators which displayed degradation or failure and hence required maintenance actions compared with sixty others which did not.