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It is well known that for long-duration space missions, there is growing need for efficient utilization of telemetry data to enhance diagnostic performance and assist the less-experienced personnel in performing monitoring and diagnosis tasks. To address this need, we have, recently, developed a systematic and transparent fault diagnosis methodology within a hierarchical fault diagnosis framework for satellites formation flight. We developed our proposed hierarchical decomposition framework through a novel Bayesian network-based model, namely component dependence model (CDM). In this paper, we investigate the verification and validation of the CDM for fault diagnosis in satellites formation flight. We propose and develop a sensitivity analysis to verify the CDM by taking advantage of our systematic CDM development methodology. The proposed verification method satisfies the unique requirement of identifying CDM sensitivity when diagnostic performances of the algorithms that are deployed at one or more nodes of the CDM change. This implies that our verification approach and analysis are different from traditional sensitivity analysis that uses proportional scaling which is not applicable to the CDM methodology. Furthermore, in such analysis, a change in the model parameters under consideration is, typically, due to a change in the subjective judgment of an expert whose opinion is used in model development as opposed to the changes due to diagnostic performance variations. We demonstrate the proposed verification approach by using synthetic formation flight data, and show that our CDM development method does not lead to a fault diagnosis model that is sensitive to small variation in its parameters.