The primary goal of this paper was to examine the validity of four sets of hypotheses that stem from four central research questions:
1.) Can prognostics be successfully implemented on electronic circuit cards that have embedded diagnostics?
The reason this research question was chosen is that prior research indicates that there have been no successful instances where prognostics have been implemented on electronic circuit cards. The cases that were presented in the prior research were all unsuccessful because of modeling uncertainties, the lack of intelligent algorithms, and the lack of technical exploration in electronic prognostics (Saxena, Celaya, Saha, Saha, & Goebel, 2010; Tang, Kacprzynski, Goebel, & Vachtsevanos, 2009). The proposed method uses simpler (lighter) algorithms that are customizable to the end-user needs. Successfully answering this question will allow electronics to become cheaper, more reliable, and better position businesses to increase their profit margins. The expected outcome of this question is that prognostics can be implemented on electronic circuit cards through the use of the proposed simpler (customizable) algorithms.
2.) Will implementing prognostics affect the overall delivery time-to-market?
The reason this research question was chosen is that if a business is in a market where the delivery time-to-market is short, they may not be able to see the value-add or the return on their investment for implementing prognostics. The reason the answer to this question is important is that design engineers may be tied down trying to design in the parameters of prognostics. The time spent on adding in prognostics to a design (time when the product is not being sold), potentially ...
... middle of paper ...
...t will bring value-add ultimately to the end-user.
H4o: The use of the proposed prognostic-algorithms will not allow design engineers and businesses to customize areas of focus (reliability, cost, lowest/highest component replacement, lowest/highest system replacement, and other areas of concern) and the algorithms will not bring value-add to the end-user.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Evaluating prognostics performance for algorithms incorporating uncertainty estimates. IEEE Aerospace Conference Journal, 1372(1), 12-23. doi: 10.1109/AERO.2010.5446828
Tang, L., Kacprzynski, G., Goebel, K., & Vachtsevanos, G. (2009). Methodologies for uncertainty management in prognostics. IEEE Aerospace Conference Journal 1361(4), 11-12. doi: 10.1109/AERO.2009.4839668
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