The Curiosity Mars Rover's Fault Protection Engine

Benowitz, Ed

doi:10.1109/smc-it.2014.16

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…ICPS demand sophisticated fault management if they are to operate for the maximum possible uptime without interruption [33,34]. Self-managing fault diagnostic engines now ensure the reliability of interplanetary spacecraft and smart factories, often in situations where humans are not able to be present when things go wrong [35,36]. A fault is defined as any change in the operation of an ICPS that leads to unacceptable behavior or degraded performance [37].…”

Section: Fault Identification and Diagnosis In Icpsmentioning

confidence: 99%

“…In most practical instances, the engine is deployed on its own hardware, separate from the platform the FB application is executing on. The rational for creating stand-alone fault management engines is discussed in depth in Benowitz's description of the NASA Curiosity Mars Rover [36]. Goupil et al present a similar rational for the Airbus fault engine [80].…”

Section: Diagnostic Points and Telemetrymentioning

confidence: 99%

“…However, that is unlikely to be a viable option in most cases. Separate and redundant fault diagnostic engines that operate reliably while other systems are failing are the norm in avionic and aerospace control systems [36,80]. Where large ICPS are being diagnosed, the engine must provide sufficient resources to ensure that the agents can perform all tasks within the required time window.…”

Section: Iso 422 Performance Efficiencymentioning

confidence: 99%

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Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

2021

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IEC 61499 is a reference architecture for constructing Industrial Cyber-Physical Systems (ICPS). However, current function block development environments only provide limited fault-finding capabilities. There is a need for comprehensive diagnostic tools that help engineers identify faults, both during development and after deployment. This article presents the software architecture for an agent-based fault diagnostic engine that equips agents with domain-knowledge of IEC 61499. The engine encourages a Model-Driven Development with Diagnostics methodology where agents work alongside engineers during iterative cycles of design, development, diagnosis and refinement. Attribute-Driven Design (ADD) was used to propose the architecture to capture fault telemetry directly from the ICPS. A Views and Beyond Software Architecture Document presents the architecture. The Architecturally-Significant Requirement (ASRs) were used to design the views while an Architectural Trade-off Analysis Method (ATAM) evaluated critical parts of the architecture. The agents locate faults during both early-stage development and later provide long-term fault management. The architecture introduces dynamic, low-latency software-in-loop Diagnostic Points (DPs) that operate under the control of an agent to capture fault telemetry. Using sound architectural design approaches and documentation methods, coupled with rigorous evaluation and prototyping, the article demonstrates how quality attributes, risks and architectural trade-offs were identified and mitigated early before the construction of the engine commenced.

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Section: Fault Identification and Diagnosis In Icpsmentioning

confidence: 99%

Section: Diagnostic Points and Telemetrymentioning

confidence: 99%

Section: Iso 422 Performance Efficiencymentioning

confidence: 99%

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Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

2021

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“…The research focus on flight control, high-dependability and predictive fault management aligns with the observations from the studies at the highest TRL discussed in this section. Benowitz [117] profiles the Fault Protection Engine currently used by the Mars Curiosity rover.…”

Section: Studies From Aerospace and Avionicsmentioning

confidence: 99%

“…Provan (2014) discusses how acceptable inputs can be modeled, an important pre-requisite to detecting misbehavior. Moni-tors (Benowitz, 2014) are code within a fault identification system that is responsible for detecting anomalous situations or behavior. Similarly, Monitor-Based Oracles provide ways of both capturing and evaluating possible fault occurrence (Yen et al, 2017;Abel et al, 2013;Schoeller et al, 2007).…”

Section: Physics-based Model-driven Diagnosticsmentioning

confidence: 99%

Finding faults: A scoping study of fault diagnostics for Industrial Cyber–Physical Systems

Dowdeswell

Sinha

MacDonell

2020

Journal of Systems and Software

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis

et al. 2022

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The purpose of this article is to study the issues of industrial maintenance, one of the critical drivers of Industry 4.0 (I4.0), which has contributed to the advent of new industrial challenges. In this context, predictive maintenance 4.0 (PdM4.0) has seen a significant progress, providing several potential advantages among which: increase of productivity, especially by improving both availability and quality and ensuring cost-saving through automated processes for production systems monitoring, early detection of failures, reduction of machine downtime, and prediction of equipment life. In the research work carried out, we focused on bibliometric analysis to provide beneficial guidelines that may help researchers and practitioners to understand the key challenges and the most insightful scientific issues that characterize a successful application of Artificial Intelligence (AI) to PdM4.0. Even though, most of the exploited articles focus on AI techniques applied to PdM, they do not include predictive maintenance practices and their organization. Using Biblioshiny, VOSviewer, and Power BI tools, our main contribution consisted of performing a Bibliometric study to analyze and quantify the most important concepts, application areas, methods, and main trends of AI applied to real-time predictive maintenance. Therefore, we studied the current state of research on these new technologies, their applications, associated methods, related roles or impacts in developing I4.0. The result shows the most common productive sources, institutes, papers, countries, authors, and their collaborative networks. In this light, American and Chinese institutes dominate the scientific debate, while the number of publications in I4.0 and PdM4.0 is exponentially growing, particularly in the field of data-driven, hybrid models, and digital twin frameworks applied for prognostic diagnostic or anomaly detection. Emerging topics such as Machine Learning and Deep Learning also significantly impacted PdM4.0 development. Subsequently, we analyzed factors that may hinder the successful use of AI-based systems in I4.0, including the data collection process, potential influence of ethics, socio-economic issues, and transparency for all stakeholders. Finally, we suggested our definition of trustful AI for I4.0.

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The Curiosity Mars Rover's Fault Protection Engine

Cited by 7 publications

References 6 publications

Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

Finding faults: A scoping study of fault diagnostics for Industrial Cyber–Physical Systems

Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis

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