Survival Analysis of Computers at a University’s Computer Laboratory and Implication on Maintainability

Автор: Timothy Simpson, Joseph Danso, John Awuah Addor, Stephen Graham Anaman

Журнал: International Journal of Education and Management Engineering @ijeme

Статья в выпуске: 2 vol.14, 2024 года.

Бесплатный доступ

Research has shown that, after extensive use, digital devices like computers often suffer performance declines, and some even experience sudden, complete breakdowns without warning. This phenomenon is particularly disturbing for individuals who heavily rely on these devices to carry out critical tasks. Although researchers have extensively probed the causes of computer breakdowns, detailed parameters influencing the lifespan of computers remain underexplored. This paper, therefore, aims to estimate the probability associated with the continuous functioning or failure of a computer system over a specified duration, and to examine risk factors associated with failure. Delving into the mysteries of computer longevity, data on 100 computers in a designated lab at an academic environment were examined. Data was drawn from maintenance records as well as in-depth hardware assessments. Analysis revealed that, after a 4-year period of active usage, 73 of the computers remained operational, while 27 had malfunctioned. Survival analysis methods were employed to determine the probability of computers failing at specific points in time and to identify various factors contributing to early computer failure. The findings disclosed that at the two-year mark, the probability of computers remaining operational is 80%, decreasing to 62% at the three-year juncture. The median survival time was established at 3 years and 4 months. Furthermore, an analysis of causative factors revealed that computers with faulty motherboards and power supply units associates with a lower rate of survival, while computers with issues of hard drives, operating systems, and miscellaneous components has a higher rate of survival. This study provides comprehensive data-driven evidence that offers insights on the need to implement maintenance strategies to proactively extend the lifespan of computers.

Еще

Survival Analysis, reliability, computers, failure, lab, Cox Proportional Hazard Model

Короткий адрес: https://sciup.org/15019304

IDR: 15019304   |   DOI: 10.5815/ijeme.2024.02.02

Список литературы Survival Analysis of Computers at a University’s Computer Laboratory and Implication on Maintainability

  • Haidar, A. (2021). Computers in our daily lives. International Journal of Computer Science and Information Technology Research, 9 (2), pp11-17.
  • Veemat M, E., Sebok S. L., Freund S. M..(2018) Discovering Computers, Digital Technology,Data and Devices. 16th Edition. Shelly and Cashman Series.
  • Iyer, R. K., &Velardi, P. (1985). Hardware-Related Software Errors: Measurement and Analysis. IEEE Transactions on Software Engineering, 11(2) , pp 223-231.
  • Wang, G., Xu, W., & Zhang, L. (2017). What Can We Learn from Four Years of Data Center Hardware Failures? 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks,2017
  • Sheilds, C. (2004). Technology: Scientific American. Retrieved January 10, 2023, from Scientifi American: https://www.scientificamerican.com/article/why-do-computers-crash/
  • Nussbaumer, R. (2019). Computers: use 'em, think about 'em, fix 'em, break 'em, program 'em, love 'em. Quora.
  • Hosen, M., Uddin M, N., Hossain, S., Islam, M. A, Ahmad, A (2022). The impact of COVID-19 on tertiary educational institutions and students in Bangladesh. Cell Press Heliyon , 8, https://doi.org/10.1016/j.heliyon.2022.e0880
  • Takoradi Technical University, About Us. [Online] Available at: https://www.ttu.edu.gh/about-us/
  • Sinha, S., Goal, N. K., & Mall, R. (2019). Early prediction of reliability and availability of combined hardware-software systems based on functional failures. Journal of Systems Architecture ,92, pp 23-38.
  • Imouokhome, Desmond, M., &Osubor, V. (2018). Causes of Failure and Breakdown of Personal Computers in Nigeria. University of Sindh Journal of Information and Communication Technology USJICT), 2 (2).
  • Sun, X., Chakrabarty, K., Huang, R., Chen, Y., Zhao, B., Cao, H., et al. (2019). System-level hardware failure prediction using deep learning. 56th ACM/IEEE Design Automation Conference (DAC), pp. 1-6.
  • Kleinbaum, David G.; Klein, Mitchel (2012), Survival analysis: A Self-learning text (Third ed.), Springer, ISBN 978-1441966452
  • Lánczky, A., &Győrffy, B. (2021). Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. Journal of Medical Internet Research, 23.
  • Tang, Z., Zhou, C., Jiang, W., & Zhou, W. (2014). Analysis of Significant Factors on Cable Failure Using the Cox Proportional Hazard Model. IEEE Transactions on Power Delivery, 29 (2), pp 951-956.
  • Ahmad T, Munir A, Bhatti SH, Aftab M, Raza MA. (2017). Survival analysis of heart failure patients: A case study. PLoSOne.doi: 10.1371/journal.pone.0181001.
  • Caselli, S., Corbetta, G., Cucinelli, D., &Rossolini, M. (2021). A survival analysis of public guaranteed loans: Does financial intermediary matter? Journal of Financial Stability.
  • Ali, S. M., Hoq, S. M., Bari, A. B., Kabir, G., & Paul, S. K. (2022). Evaluating factors contributing to the failure. 17 (3), 17.
  • Katchova, A. (2013). Survival Analysis.
  • Turkson A. J, Simpson T., Addor J. A. (2021). An Introspective Overview of the Dynamics of Recurrent Events Data Analysis. Asian Journal of Probability and Statistics. AJPAS. doi:10.9734/ajpas/2021/v15i430371
  • Davidson-Pilon, C. (2019). Survival analysis in Python. Journal of Open Source Software, 4 (40), 1317.
  • Limoncelli, T, A., Hogan, C. J., Chalup, S, R. (2016) Practice of System and Network Administration, The: DevOps and other Best Practices for Enterprise IT, Volume 1, 3rd Edition, Addison-Wesley Professional Publishers.
  • Allan, R. A. (2011). A history of personal computer: the people and the technology. Allan Publications, London
Еще
Статья научная