Parametric Testing of Launch Vehicle FDDR Models

Abstract

For the safe operation of a complex system like a (manned) launch vehicle, real-time information about the state of the system and potential faults is extremely important. The on-board FDDR (Failure Detection, Diagnostics, and Response) system is a software system to detect and identify failures, provide real-time diagnostics, and to initiate fault recovery and mitigation. The ERIS (Evaluation of Rocket Integrated Subsystems) failure simulation is a unified Matlab/Simulink model of the Ares I Launch Vehicle with modular, hierarchical subsystems and components. With this model, the nominal flight performance characteristics can be studied. Additionally, failures can be injected to see their effects on vehicle state and on vehicle behavior. A comprehensive test and analysis of such a complicated model is virtually impossible. In this paper, we will describe, how parametric testing (PT) can be used to support testing and analysis of the ERIS failure simulation. PT uses a combination of Monte Carlo techniques with n-factor combinatorial exploration to generate a small, yet comprehensive set of parameters for the test runs. For the analysis of the high-dimensional simulation data, we are using multivariate clustering to automatically find structure in this high-dimensional data space. Our tools can generate detailed HTML reports that facilitate the analysis.

Automated Search-Based Robustness Testing for Autonomous Vehicle Software

“…For simple software units where a small number of input values have been discretized into a small set of specific enumerations, it may be possible to test all possible combinations of input values. Combinatorial testing was performed in [9] to support testing of a launch vehicle failure detection, diagnostics, and response system using a simplified vehicle simulation. All possible test cases were generated by varying model input variables (setting variables to maximum, nominal, or minimum values) and failure mode insertions (with fault occurrence times restricted to occur at specific points in time rather than all possible time).…”

Integrated Software and Sensor Health Management for Small Spacecraft

“…This statistical approach combines n-factor combinatorial exploration with advanced data analysis [16] to exercise the SSHM model with wide ranges of sensor inputs and internal parameters. This approach scales to large systems, like fault detection models for the Ares I rocket [17] or hardware Health Management systems [18]. Finally, sensitivity analysis for Bayesian Networks [4] is useful to assess the quality of the model parameters.…”

Verification and Validation of System Health Management Models using Parametric Testing

“…For details on the use of the HTML reports see. 6 All figures in this paper have been extracted from these autogenerated reports.…”

“…We are using Parametric Testing (PT), which uses a combination of n-factor and Monte Carlo methods to exercise the HM model with variations of perturbed parameters. This technique, which has been successfully applied to various application areas 6,7 avoids the excessive numbers of cases caused by combinatorial exploration while at the same time yielding good coverage of the parameter space. The result of model analysis is a large, high-dimensional data set.…”