Towards a Fault Management Analysis Tool for Model Centric Systems Engineering

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 5, 2024
Ksenia Kolcio Maurice Prather David Wagner Maged Elaasar Narek Shougarian

Abstract

In an effort to effectively develop more complex spacecraft fault management (FM) systems new technologies are sought to enable rapid diagnostic model generation and validation, and provide tools to perform FM analyses Model-based Systems Engineering approaches to FM system development are uniquely suited to be combined with model-based tools currently utilized in the design of other parts of flight systems. Combined tools utilizing information from a common system model can reduce design inconsistencies and gaps in analyses. Tighter integration of FM with other system-level and subsystem-level hardware/software development activities allows crucial redundancy and sensor placement trades to be performed earlier and throughout the mission lifecycle.

Our work has been towards the integration of a model-based fault management tool suite called MONSID®, with JPL’s Computer Aided Engineering for Systems ARchitecture (CAESAR ) platform as a way to improve FM system modeling and analysis. MONSID relies on application-specific models of the system being monitored. MONSID models consist of interconnected elements representing system hardware and measurement/command input points, called the topology. Model topology design is currently a manual process and often relies heavily on paper documentation such as hardware/software specs, engineering drawings, and interface control documents. CAESAR is a semantically- driven toolchain for model-based system engineering. At the core is a system model expressed in the Ontological Modeling Language (OML). It is intended to support semantic modeling, consistency validation, and continuous integration.

A goal of the combined toolset is to automate FM model development by directly extracting models from CAESAR and then analyzing them in MONSID. Analyses currently available in MONSID include model topology inspection and validation and fault isolation capability based on sensor placement. While we have focused on two specific tools, the integration approaches can be leveraged by other semantically driven model-centric platforms and tools.

This paper describes the evolution of our integration approaches as we evaluated different insertion points in the CAESAR toolchain with respect to MONSID model requirements. The MONSID-CAESAR tool is demonstrated on a simplified example of a spacecraft heat reclamation system. Results of the generated MONSID model are discussed, including levels of automation achieved and information surfaced to the users about the extracted model topology.

How to Cite

Kolcio, K., Prather, M., Wagner, D., Elaasar, M., & Shougarian, N. (2024). Towards a Fault Management Analysis Tool for Model Centric Systems Engineering. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.3895
Abstract 62 | PDF Downloads 36

##plugins.themes.bootstrap3.article.details##

Keywords

digital engineering, fault management analysis, model-based fault detection and isolation

References
1. Avizienis, A., (1997). "Toward systematic design of fault-tolerant systems," Computer, vol. 30, no. 4, pp. 51-58, April 1997, doi: 10.1109/2.585154.

2. Chau, S., Alkalai, L., & Tai, A. T., (2000). “Analysis of Multi-Layer Fault Tolerant COTS Architecture for Deep Space Missions,” Symposium on Application-Specific Systems and Software Engineering and Technology, Mar 24.

3. Elaasar, M., Rouquette, N., Wagner, D., Oakes, B., Hamou-Lhadj, A., & Hamdaqa, M., (2023). "openCAESAR: Balancing Agility and Rigor in Model-Based Systems Engineering", Proceedings of SAM 2023, Oct., Västerås, Sweden.

4. Kolcio, K., (2016). “Model-Based Fault Detection and Isolation System for Increased Autonomy”, AIAA SPACE 2016, AIAA SPACE Forum, (AIAA 2016-5225), Sept 13-16 , Long Beach, CA.

5. Kolcio, K., Fesq, L. M., & Mackey, R., (2017). “Model-Based Approach to Rover Health Assessment for Increased Productivity”, IEEE Aerospace Conference, Mar 5-10, Big Sky, MT.

6. Kolcio, K., Fesq, L. M., & Mackey, R., (2019). “Model-Based Approach to Rover Health Assessment - Mars Yard Discoveries”, IEEE Aerospace Conference, Mar 2-9, Big Sky, MT.

7. Kolcio; K., & Prather, M., (2023). "Implementation and Evaluation of Model-based Health Assessment for Spacecraft Autonomy", IEEE Aerospace Conference, March 4-11, Big Sky, MT.

8. Mackey, R., Nikora, A., Fesq, L. M., & Kolcio, K., (2021). “On-Board Model Based Fault Diagnosis for CubeSat Attitude Control Subsystem: Flight Data Results”, IEEE Aerospace Conference, Mar 6-13, Big Sky, MT.

9. NASA Fault Management Handbook, (2012). (NASA-HDBK-1002), April 2.

10. Wagner, D., Kim-Castet, S. Y., Jimenez, A., Elaasar, M., Rouquette, N., & Jenkins, S., (2020). "CAESAR Model Based Approach to Harness Design", IEEE Aerospace Conference, Big Sky, MT, doi: 10.1109/AERO47225.2020.9172630.
Section
Industry Experience Papers