Using Fault Augmented Modelica Models for Diagnostics

Minhas, Raj; Kleer, Johan de; Matei, Ion; Saha, Bhaskar; Janssen, Bill; Tolga, Daniel G. Bobrow

doi:10.3384/ecp14096437

Cited by 22 publications

(12 citation statements)

References 11 publications

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“…fmdtools has additionally since expanded in scope to include not just fault propagation tools but the necessary analysis and visualization tools needed to interpret Toolkit Causality Representation Model Format(s) Availability Use in design HiP-Hops (Papadopoulos & McDermid, 1999) Dynamic simulation with failure logic Simulink, Simula-tionX, AADL, etc. Commercial Functional Hazard Assessment, Design Optimization (Papadopoulos et al, 2011) Rodon (Bunus et al, 2009) Behavioral constraint network with failure logic Modelica-like Rodelica model Commercial Model-based engineering process (Lunde et al, 2006) Modelica fault libraries (van der Linden, 2014;Minhas et al, 2014;Gundermann et al, 2019) Undirected behavioral/failure logic Modelica Open Source Design exploration (Lattmann et al, 2014) OpenErrorPro (Morozov et al, 2019) Probabilistic markov chain Simulink, Stateflow, UML, SysML, AADL Open Source Model-based reliable system design (Morozov et al, 2018) SHyFTOO (Chiacchio et al, 2020) Dynamic simulation with probabilistic hybrid fault tree Simulink, MAT-LAB code Open Source Model-based design (Chiacchio et al, 2019) OpenCossan (Patelli et al, 2018) Probabalistic semi-markov transitions and/or external simulation…”

Section: Related Workmentioning

confidence: 99%

Fmdtools

Hulse

Walsh

Dong

et al. 2021

IJPHM

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Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one’s ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many theoretical frameworks have been presented for early hazard assessment, most currently-available modelling tools are meant for the later stages of design. Given the wide adoption of Python in the broader research community, there is an opportunity to create an environment for researchers to study the resilience of different PHM technologies in the early phases of design. This paper describes fmdtools, an attempt to realize this opportunity with a set of modules which may be used to construct different design models, simulate system behaviors over a set of fault scenarios and analyze the resilience of the resulting simulation results. This approach is demonstrated in the hazard analysis and architecture design of a multi-rotor drone, showing how the toolkit enables a large number of analyses to be performed on a relatively simple model as it progresses through the early design process.

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Section: Related Workmentioning

confidence: 99%

Fmdtools

Hulse

Walsh

Dong

et al. 2021

IJPHM

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“…Das wirtschaftliche und gesellschaftliche Potenzial für solche neuen Ansätze ist gewaltig (Frauhofer IAIS 2013). Bisher stoßen datengetriebene Ansätze trotzdem auf viel Skepsis, in den meisten Projekten zur Optimierung und Diagnose werden physikalische und Verhaltensmodelle verwendet, die von Hand erstellt worden sind (Minhas et al 2014). Bei einem Antrieb wird dieser modelliert; bei einem Reaktor werden dessen chemische und physikalische Prozesse modelliert.…”

Section: Motivationunclassified

Konzeptualisierung als Kernfrage des Maschinellen Lernens in der Produktion

Niggemann¹,

Biswas²,

Kinnebrew³

et al. 2020

Springer Reference Technik

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Die Mehrheit der Projekte zur Überwachung und Diagnose Cyber-Physischer Systeme (CPS) beruht heute auf von Experten erstellten Modellen. Diese Modelle sind jedoch nur selten verfügbar, sind oft unvollständig, schwer zu überprüfen und zu warten. Datengetriebene Ansätze sind eine viel versprechende Alternative: Diese nutzen die großen Datenmengen die heutzutage in CPS gesammelt werden. Algorithmen verwenden die Daten, um die zur Überwachung notwendigen Modelle automatisch zu lernen. Dabei sind mehrere Herausforderungen zu bewältigen, wie zum Beispiel die Echtzeit-Datenerfassung und Speicherung, Datenanalyse, Mensch-Maschine Schnittstellen, Feedback-und Steuerungsmechanismen. In diesem Beitrag wird eine kognitive Referenzarchitektur vorgeschlagen, um diese Herausforderungen in Zukunft einfacher zu lösen. Anhand dieser Referenzarchitektur wird eine Schlüsselfrage diskutiert: Der Übergang von subsymbolische Informationen wie sie für das maschinelle Lernen typisch sind

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“…This approach uses a separate behavioral model simulation to determine fault propagation and fault effect. Typically, the modeling language Modelica is applied to simulate failure scenarios [48,49], however, system models cannot be automatically constructed from a description of the functional structure of a system and therefore it may not be useful in FFIP where multiple designs are investigated.…”

Section: Failure Analysis and Functional Modelsmentioning

confidence: 99%

Resilient System Design Using Cost-Risk Analysis With Functional Models

Keshavarzi

McIntire

Goebel

et al. 2017

Volume 2A: 43rd Design Automation Conference

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This paper presents a framework to compare the resiliency of different designs during the conceptual design, when information about implementation details is unavailable. We apply the Inherent Behavioral Functional Model (IBFM) tool to develop an initial functional model for a system and simulate the failure behavior. The simulated failure scenarios provide us the information on the unique failure propagation paths and the end state/final behavior of the system assigned to each failure. Each failure path is caused by injecting one or multiple simultaneous faults into the functional model. Within this framework, we generate a population of functional models from a baseline seed model, and evaluate its potential failure scenarios. We also develop a cost-risk model to compare resiliency of different designs, and produce a preference ranking. select the most resilient one, based upon the cost-risk objective. The risk is calculated based on the probability of having an undesired end state for each design, and a consequential cost is assigned to each failure to quantify the cost-risk for a given design. In this paper, we implement and demonstrate the proposed method on the design of a resilient mono-propellant system.

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Using Fault Augmented Modelica Models for Diagnostics

Cited by 22 publications

References 11 publications

Fmdtools

Fmdtools

Konzeptualisierung als Kernfrage des Maschinellen Lernens in der Produktion

Resilient System Design Using Cost-Risk Analysis With Functional Models

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