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Essay on Implementing Prognostics Within the Telecommunications Industry

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The results show that four categories (non-reliable testing, unintelligent prognostic algorithms, inadequate fault detection, and data archiving) were affecting mission readiness. The findings proved to be logical because the government has adopted this medium for diagnostics as a solution for legacy test system upgrades but they are examining ways to implement real time prognostics. The author’s conclusions were sound and lucid; and he developed a medium to reduce rework times, reduce yearly preventive maintenance, reduce repair times, and finally serve as a catalyst for accurate fault detection.

This author examines a specific problem with prognostic-based algorithms, which is common to other researcher’s findings. The author has published dozens of scholarly works in the field of test. The intended audience is test engineers that are looking to expand the conventional limitations of diagnostics.


Erto, P., Barone, S., & Palumbo, B. (2006). A robust calibration methodology for an on-board diagnostic car system. Quality Engineering, 18(2), 145-159. doi:10.1080/08982110600567491
The scope of this research was to scrutinize pollutants through on–board diagnostics (OBDs) in automobiles. On-board diagnostics have been embedded in emission control systems to ensure pollutants do not escape the exhaust system. When the authors explored the emission control system, the main mechanism was determined to be the catalytic converter. The converter released and transformed harmful pollutants into admissible substances. To certify this degraded reformation the catalytic converter had to maintain a salubrious air to fuel ratio.

The method used was to take an oxygen sensor and have it monitor the process and affirm the r...


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... & Wagenknecht, L. (2004). Metabolic and inflammation variable clusters and prediction of type 2 diabetes: Factor analysis using directly measured insulin sensitivity. Diabetes 53(7), 1773-1781. Retrieved from http://search.proquest.com/docview/216487181?accountid=28180


Kelishadi, R., Ardalan, G., Adeli, K., Motaghian, M., Majdzadeh, R., Delavari, A., & Namazi, R. (2007). Factor analysis of cardiovascular risk clustering in pediatric metabolic syndrome: CASPIAN study. Annals of Nutrition & Metabolism, 51(3), 208-215. Retrieved from http://search.proquest.com/docview/232081060?accountid=28180


Kumaresan, M., & Riyazuddin, P. (2008). Factor analysis and linear regression model (LRM) of metal speciation and physico-chemical characters of groundwater samples. Environmental Monitoring and Assessment 138(1), 65-79. doi: 10.1007/s10661-007- 9761-8


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