Demonstrating Assurance of Model-Based Fault Diagnosis Systems on an Operational Mission

被引:3
|
作者
Nikora, Allen [1 ]
Aleem, Mishaal [1 ]
Mackey, Ryan [1 ]
Fesq, Lorraine [1 ]
Chung, Seung [1 ]
Kolcio, Ksenia [2 ]
Prather, Maurice [2 ]
Litke, Matthew [2 ]
机构
[1] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91125 USA
[2] Okean Solut Inc, 1463 East Republican St,32A, Seattle, WA 98112 USA
来源
2020 IEEE AEROSPACE CONFERENCE (AEROCONF 2020) | 2020年
关键词
D O I
10.1109/aero47225.2020.9172282
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Developers of robotic scientific and commercial spacecraft are trending towards use of onboard autonomous capabilities for responding quickly to dynamic environments and rapidly changing situations. These capabilities need to know the state of the spacecraft's health. Model-based fault diagnosis (MBFD) is an approach to estimating health by continuously verifying accurate behavior and diagnosing off-nominal behavior. Proper functioning of MBFD depends on 1) the quality of the diagnostic system model that is analyzed and compared to commands and onboard measurements to estimate a system's health state, and 2) the correct functionality of the diagnosis engine interrogating the model and comparing its analyses to observed system behavior. Our goal is to develop Verification and Validation (V&V) techniques for MBFD to provide future missions sufficient confidence in its functionality and performance to deploy it on the systems they develop. Our work has been focused on infusing the techniques we developed earlier to an operational mission. First, we are constructing diagnostic models of a spacecraft attitude control system and updating our diagnostic engine so they can be demonstrated aboard the Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) mission, an operational spacecraft for which experiments in autonomy are being planned and executed, using the V&V techniques we have previously developed to assure they are both correct and complete. Since it is nearing the end of its life, ASTERIA provides a unique opportunity to demonstrate MBFD since the monitored components are expected to fail. Our demonstration will give system developers additional confidence to make timely, informed MBFD deployment decisions. Second, we will be completing performance assessments of the diagnostic engine/diagnostic model ensemble both on the flight system and ground-based testbeds to gain confidence in MBFD's ability to run successfully in a spacecraft's resource-constrained environment without adversely affecting other on-board activities. Finally, we are capturing our experience in preparing this demonstration in a set of checklists and guidance documents. Current practice includes high-level institutional guidance documents and standards, but at a high level of abstraction that does not necessarily address specific MBFD concerns. The purpose of the new checklists is to provide future mission developers clear, unambiguous, procedure-oriented guidance on assuring MBFD. This paper describes our work in these areas. For the first area, we describe the diagnostic models and updated diagnostic engine that will be used for the on-board demonstration. We describe how the V&V techniques we developed earlier are used to assure model and engine correctness and completeness. For the second area, we identify the performance measurement and assessment techniques used to characterize the diagnostic engine and diagnostic models, and discuss the effect of measured performance on overall mission operation. Finally, we present the checklist and guidance documents and describe how they meet the goals of providing system developers with clear, unambiguous, procedure-oriented guidance on MBFD assurance. We show how the techniques we have developed map into those artifacts.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] A model-based approach to fault diagnosis of FMS
    ChengLeong, A
    LiPheng, K
    ETFA '96 - 1996 IEEE CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, PROCEEDINGS, VOLS 1 AND 2, 1996, : 254 - 260
  • [42] A model-based approach to robot fault diagnosis
    Liu, HH
    Coghill, GM
    KNOWLEDGE-BASED SYSTEMS, 2005, 18 (4-5) : 225 - 233
  • [43] Model-Based Fault Diagnosis Methods: A Survey
    Cheng Yu
    Wang Wu
    Cui Fujun
    Yang Fuwen
    PROCEEDINGS OF THE 27TH CHINESE CONTROL CONFERENCE, VOL 6, 2008, : 17 - 20
  • [44] Model-Based Fault Diagnosis with Fractional Models
    Kopka, Ryszard
    ADVANCES IN MODELLING AND CONTROL OF NON-INTEGER ORDER SYSTEMS, 2015, 320 : 257 - 263
  • [45] Model-based fault diagnosis in technical processes
    Frank, P.M.
    Ding, S.X.
    Marcu, T.
    2000, Inst of Measurement & Control, London, United Kingdom (22)
  • [46] Model-based fault diagnosis method for gyro
    Li, Gan-hua
    Li, Jian-cheng
    Fan, Meng-hai
    Cao, Ya-ni
    Xu, Min-qiang
    Wei, Jun
    Liang, Min
    Dong, Li
    PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019), 2019, : 1004 - 1007
  • [47] Model-Based Fault Diagnosis of Selective Catalytic Reduction Systems for Diesel Engines
    Chen, Rui
    Wang, Xinlei
    SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-ELECTRONIC AND ELECTRICAL SYSTEMS, 2014, 7 (02): : 449 - 453
  • [48] A model-based online fault detection and diagnosis strategy for centrifugal chiller systems
    Cui, JT
    Wang, SW
    INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 2005, 44 (10) : 986 - 999
  • [49] Model-based fault diagnosis methods for systems with stochastic process-A survey
    Zhao, Zhen
    Liu, Peter Xiaoping
    Gao, Jinfeng
    NEUROCOMPUTING, 2022, 513 : 137 - 152
  • [50] Fault diagnosis for a kind of nonlinear systems by using model-based contribution analysis
    Liu, Hai
    Zhong, Maiying
    Liu, Yang
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2018, 355 (16): : 8158 - 8176