A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems

Daigle, Matthew; Roychoudhury, Indranil; Biswas, Gautam; Koutsoukos, X D; Patterson-Hine, Ann; Poll, Scott

doi:10.1109/tsmca.2010.2052038

Cited by 78 publications

(36 citation statements)

References 25 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We considered only single faults here, and in the future we will extend the framework to multiple faults. Sensor faults were not considered here, but can be easily incorporated into a model-based framework by including models of the sensors [12]. Using the physics model for prognostics and loading optimization is also of interest.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Model-based diagnostics for propellant loading systems

Daigle

Foygel

Smelyanskiy

2011

2011 Aerospace Conference

View full text Add to dashboard Cite

Abstract-The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

show abstract

Section: Discussionmentioning

confidence: 99%

“…We use the Z-test for robust fault detection using a set of sliding windows, as described in [11,12]. The current mean of a residual signal, µ r (t), is estimated over a small window W 2 :…”

Section: Fault Detectionmentioning

confidence: 99%

Model-based diagnostics for propellant loading systems

Daigle

Foygel

Smelyanskiy

2011

2011 Aerospace Conference

View full text Add to dashboard Cite

show abstract

“…1 ModelPlex monitors in a Simplex [39] setting: a fallback action gets executed when sensor readings and control decisions do not comply with a monitor level challenges w.r.t. monitor implementation and violation cause diagnosis are discussed elsewhere [8,21,45].…”

Section: Introductionmentioning

confidence: 99%

ModelPlex: verified runtime validation of verified cyber-physical system models

Mitsch

Platzer

2016

Form Methods Syst Des

View full text Add to dashboard Cite

Formal verification and validation play a crucial role in making cyber-physical systems (CPS) safe. Formal methods make strong guarantees about the system behavior if accurate models of the system can be obtained, including models of the controller and of the physical dynamics. In CPS, models are essential; but any model we could possibly build necessarily deviates from the real world. If the real system fits to the model, its behavior is guaranteed to satisfy the correctness properties verified with respect to the model. Otherwise, all bets are off. This article introduces ModelPlex, a method ensuring that verification results about models apply to CPS implementations. ModelPlex provides correctness guarantees for CPS executions at runtime: it combines offline verification of CPS models with runtime validation of system executions for compliance with the model. ModelPlex ensures in a provably correct way that the verification results obtained for the model apply to the actual system runs by monitoring the behavior of the world for compliance with the model. If, at some point, the observed behavior no longer complies with the model so that offline verification results no longer apply, ModelPlex initiates provably safe fallback actions, assuming the system dynamics deviation is bounded. This article, furthermore, develops a systematic technique to synthesize provably correct monitors automatically from CPS proofs in differential dynamic logic by a correct-by-construction approach, leading to verifiably correct runtime model validation. Overall, ModelPlex generates provably correct monitor conditions that, if checked to hold at runtime, are provably guaranteed to imply that the offline safety verification results about the CPS model apply to the present run of the actual CPS implementation.

show abstract

“…In the work of Dearden and Clancy (2002), the particle filter is based on importance sampling to ensure that fault modes obtain enough particles. On the other hand, hybrid diagnosis approaches (Bayoudh et al, 2008;Daigle et al, 2010) focus on the discrete part of the hybrid model and, using parity-space ideas (Gertler, 1991;Staroswiecki and Comet-Varga, 2001), limit the number of possible hypotheses to the number of modes in the system at each time step. However, additional state filtering operations need to be performed in order to obtain the continuous state estimate.…”

Section: Introductionmentioning

confidence: 99%

Mode set focused hybrid estimation

Rienmüller¹,

Hofbaur²,

Travé-Massuyès³

et al. 2013

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

Estimating the state of a hybrid system means accounting for the mode of operation or failure and the current state of the continuously valued entities concurrently. Existing hybrid estimation schemes try to overcome the problem of an exponentially growing number of possible mode-sequence/continuous-state combinations by merging hypotheses and/or deducing likelihood measures to identify tractable sets of the most likely hypotheses. However, they still suffer from unnecessarily high computational costs as the number of possible modes increases. Hybrid diagnosis schemes, on the other hand, estimate the current mode of operation/failure only, thus leaving the continuous evolution of the system implicit. This paper proposes a novel scheme that uses a combination of both the approaches in order to define posterior transition probabilities between the specified modes of the hybrid system, hence focusing better on relevant hypotheses. In order to demonstrate the effectiveness of the proposed method, the algorithm is applied to a satellite attitude control system and compared with existing hybrid estimation/diagnosis schemes, such as the Interacting Multiple Model (IMM) algorithm, a purely parity based method (HyDiag), and an existing hybrid Mode Estimation (hME) algorithm.

show abstract

A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems

Cited by 78 publications

References 25 publications

Model-based diagnostics for propellant loading systems

Model-based diagnostics for propellant loading systems

ModelPlex: verified runtime validation of verified cyber-physical system models

Mode set focused hybrid estimation

Contact Info

Product

Resources

About