The purpose of this presentation is to describe the current research in the use of Bayesian Network (BN) models to support Qualification Planning in order to predict the suitability of Six Degrees of Freedom (6DOF) vibration testing for qualification. Qualification includes environmental testing such as temperature, vibration, and shock to support a stochastic argument about the suitability of a design. Qualification is becoming more complex and restricted yet new technologies are available but not fully utilized. Technology has advanced to the state where 6DOF vibration shakers and control systems capable of high frequency tests are possible, but the problem using these systems is far more complex than traditional single degree of freedom tests. This challenges Systems Engineers as they strive to plan qualification in an environment where technical, environmental, and political constraints are coupled with the traditional cost, risk and schedule constraints. This research focuses on developing a predictive analysis framework for Six Degrees of Freedom (6DOF) vibration qualification. Specifically, this presentation covers the research approach utilizing Bayesian logic to identify and relate the driving factors affecting successful 6DOF tests. The presentation will cover the key factors and their inter-relationships. The goal of this research is to develop a statistically based decision aid for assessing 6DOF suitability. BN models may provide the framework to aid Systems Engineers in planning qualification efforts with complex constraints.
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