Life and Reliability Prediction for Turbopropulsion Systems

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-11-C-0168
Agency Tracking Number: N103-196-0849
Amount: $79,682.00
Phase: Phase I
Program: SBIR
Awards Year: 2011
Solicitation Year: 2010
Solicitation Topic Code: N103-196
Solicitation Number: 2010.3
Small Business Information
350 Hochberg Road, Monroeville, PA, -
DUNS: 052545431
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dennis Moore
 Sr. Manager
 (724) 998-3649
 dmoore@rjlg.com
Business Contact
 David James
Title: CFO
Phone: (724) 387-1820
Email: djames@rjlg.com
Research Institution
 Stub
Abstract
The predictability in assessing an engine"s shop direct maintenance cost is largely a result of the significant reliability improvements inherent in today"s engine technology and a result of the introduction of on-condition maintenance. Engine on-condition maintenance seeks to do away with"hard-time"interval removal and prescribes routine monitoring of key operational parameters to drive removal. A by-product of these reliability improvements and the on-condition maintenance philosophy is a greater reliance on software and statistical analysis to predict the frequency of engine shop visit events and their corresponding shop visit costs. Estimating an appropriate direct maintenance cost therefore, requires careful forecasting of the equipments on-wing life as well as an accurate assessment of its capability to achieve that life and understanding the costs involved in improving that capability. The requirement of any reliability software is to be able to statistically describe any of the data elements that can be used to drive a prediction of on-wing life and/or identify direct maintenance cost. This multi-faceted approach can then provide useful information to optimization software, cost or ATOW forecasting software or other useful tools that will provide engine program management and their customers the ability to accurately assess the condition of fielded systems, understand their capabilities and identify long term management needs to maintain and improve the performance of these systems through out their intended life and beyond.

* Information listed above is at the time of submission. *

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