A reliability trend/growth analysis methodology for satellite systems is suggested. A satellite system usually consists of many satellites successively launched over many years, and its satellites typically belong to different satellite generations. This paper suggests an approach to reliability trend/growth data analysis for the satellite systems based on grouped data and the Power Law (Crow-AMSAA) Non- Homogeneous Poisson process model, for both one (time) and two (time and generation) variables. Based on the data specifics, the maximum likelihood estimates for the Power Law model parameters are obtained. In addition, the Cumulative Intensity Function (CIF) of a family of satellite systems was analyzed to assess its similarity to that of a repairable system. The suggested approaches are illustrated by a case study based on Tracking and Data Relay Satellite System (TDRSS) and Geostationary Operational Environmental Satellite (GOES) data.
How to Cite
Weibull distribution, anomaly, multivariate regression, reliability growth
Castet, J., & Saleh, J. (2009). Geosynchronous Communication Satellite. 27th IET/AIAA international communications satellite systems conference 2009: (ICSSC 2009), Edinburgh, United Kingdom, 1-4 June 2009. (p. 431). Stevenage: IET.
Crowder, M. J., Kimber A. C., Smith, R. L., & Sweeting, T. J. (1991). Statistical analysis of reliability data. London: Chapman & Hall.
Military Specification (MIL)-HDBK-189C, Military Handbook: Reliability growth management (DOD, 2011).
Saleh, J. H., & Castet, J. (2011). Spacecraft reliability and multi-state failures a statistical approach. Chichester, West Sussex, UK: John Wiley.
Seber, G. A., & Wild, C. J. (1989). Nonlinear regression. New York: Wiley.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.