Failure Prediction Algorithms

1240 Words3 Pages

Gaining a better understanding of when electronics are about to fail can help engineers and companies be more effective in providing reliable electronics; this will also lead to a greater return on investments for businesses (Seggie, Cavusgil, & Phelan, 2007). Prior attempts of implementing algorithms have all failed due to the large bandwidth required to process the data in real time. The algorithms in this study will not require many resources and will use simple gate-logic and statistical data to alert an end-user of a potential failure. Scores, which are based on inputs from the end-user, for each of the four factors, will serve as the four independent variables for this study: (a) cost, (b) reliability, (c) component replacement, and (d) total system replacement.

Five different pieces of hardware will be subjected to the algorithms and tested five different times to validate if the prognostics are working. The prognostic-algorithms of years past are not intelligent enough to decipher large amounts of data. Van Bree, Veltman, Hendrix, and Van Den Bosch (2009) stated that rather than passively reacting to prognostic issues with limited fault-tolerances, the capabilities of a system should predict failures actively. The system should be able to reconfigure and control actions so that stability and acceptable performance of the entire system can be maintained (Van Bree et al., 2009). This research was intended to better understand the aspects of prognostics in the electronics world. This abridged literature review focuses on three key topics, which helped form and inspire the crux of the research questions: successful implementation of prognostics, time-to-market concerns, and technical risk.

Successful Implementation o...

... middle of paper ...

...issertation). Retrieved from ProQuest. (3442098)

Nadeau-Dostie, B. (2000). Design for at-speed test, diagnosis and measurement. Retrieved from http://site.ebrary.com/lib/ncent/Doc?id=10052637&ppg=25

Prichard, R. (2000). A case for embedded diagnostics. Fiber Optic Technology, 15(12), 138- 139. Retrieved from http://search.proquest.com/docview/219535540?accountid=28180

Seggie, S. H., Cavusgil, E., & Phelan, S. E. (2007). Measurement of return on marketing investment: A conceptual framework and the future of marketing metrics. Industrial Marketing Management, 36(1), 834-834. doi: 10.1016/j.indmarman.2006.11.001

Van Bree, P., Veltman, A., Hendrix, W., & Van Den Bosch, P. (2009). Prediction of battery behavior subject to high-rate partial state of charge. IEEE Vehicular Technology Society, 58(2), 1-18. doi:10.1109/TVT.2008.928005

More about Failure Prediction Algorithms

Open Document