Reliability Growth Analysis of Satellite Systems
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Abstract
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
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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.
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