A Bayesian Framework for Analysis of Pseudo-spatial Models of Comparable Engineered Systems With Application to Spacecraft Anomaly Prediction Based on Precedent Data
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To ensure that estimates of risk and reliability inform design and resource allocation decisions
in the development of complex engineering systems, early engagement in the design life cycle is
necessary. An unfortunate constraint on the accuracy of such estimates at this stage of concept
development is the limited amount of high fidelity design and failure information available on
the actual system under development. Applying the human ability to learn from experience and
augment our state of knowledge to evolve better solutions mitigates this limitation. However, the
challenge lies in formalizing a methodology that takes this highly abstract, but fundamentally
human cognitive, ability and extending it to the field of risk analysis while maintaining the tenets
of generalization, Bayesian inference, and probabilistic risk analysis.
We introduce an integrated framework for inferring the reliability, or other probabilistic
measures of interest, of a new system or a conceptual variant of an existing system. Abstractly, our
framework is based on learning from the performance of precedent designs and then applying the
acquired knowledge, appropriately adjusted based on degree of relevance, to the inference process.
This dissertation presents a method for inferring properties of the conceptual variant using a
pseudo-spatial model that describes the spatial configuration of the family of systems to which the
concept belongs. Through non-metric multidimensional scaling, we formulate the pseudo-spatial
model based on rank-ordered subjective expert perception of design similarity between systems
that elucidate the psychological space of the family. By a novel extension of Kriging methods for
analysis of geospatial data to our "pseudo-space of comparable engineered systems", we develop a
Bayesian inference model that allows prediction of the probabilistic measure of interest.