A Statistical Analysis and Predictive Modeling of Safing Events for
Interplanetary Spacecraft
Author(s)
Pujari, Swapnil R.
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Abstract
Unexpected spacecraft failures and anomalies may
prompt autonomous on-board systems to change a spacecraft’s
state to a ‘safe mode’ in order to isolate and resolve the problem.
Future interplanetary missions such as Psyche and the proposed
Next Mars Orbiter mission concept, plan to use solar electric
propulsion on-board. Continuous operation of the thrusters is
necessary in order to achieve their mission objectives. The mo tivation for this paper stems from a need to better predict safing
events based on various mission factors such as mission class,
destination, duration, etc. Modeling spacecraft inoperability
due to a spacecraft entering safe mode is imperative in order
to appropriately allocate spacecraft margins and shape design
& operations requirements. This paper contributes to the area
of safing events by further analyzing trends and dependencies
within the available data subsets, and develops predictive models
of frequency and recovery times of safing events for interplane tary spacecraft missions.
First, the full safing event dataset is split into multiple subsets
based on various mission classifiers. By employing the Chi Squared hypothesis test, the degree of dependency between
classifiers is assessed. A parametric analysis is conducted using
a single and mixture of two Weibull distributions. The optimal
parameters that would best fit the full dataset and subsets
are computed by a maximization likelihood algorithm. The
mean square error and Akaike Information Criteria represent
goodness-of-fit criteria for the computed distributions; insight
into any inherent bi-modal behavior is identified through these
criteria. A supervised learning algorithm is utilized in captur ing and understanding relationships between input and output
variables, and utilizing these to predict unknown outcomes.
For the safing event database, two Gaussian process models
are trained, tested, and deployed: one for time-between-events
and the other for recovery durations. By incorporating these
Gaussian Process models into a mission simulation framework,
a Monte Carlo simulation of the likelihood of inoperability
rates is conducted to robustly predict safing events. A greater
understanding of the safing event dataset through statistical &
parametric analyses, and the development of a Gaussian Process
model for predictions enables interplanetary mission planners to
make more informed decisions during spacecraft development
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Date
2018-04-27
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Text
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Masters Project
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