Model-Based Fault Detection and Diagnosis System for NASA Mars Subsurface Drill Prototype

Balaban, Edward; Cannon, Howard; Narasimhan, Sriram; Brownston, Lee

doi:10.1109/aero.2007.352947

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…sample collection) being interrupted or otherwise limited by power/resource drain. Model-based algorithmic approaches, however, rely on pre-programmed knowledge engines instead [15], and thus keep down the burden of computation while still maintaining accuracy. There is, arguably, a high level of both consistency and accuracy in the knowledge engine's performance so long as the knowledge base that the knowledge engine is working off of is complete.…”

Section: Instrument Calibrationmentioning

confidence: 99%

Defining the solution space for autonomous control of in-situ astrobiology missions (IAC-22-A7,3,13,x69116)

Singam¹

2022

Preprint

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In-situ astrobiology missions, particularly those studying planetary bodies in the outer solar system and beyond, pose a unique challenge in terms of command, control, and communication. In addition to facing signal delays when attempting long-distance communications, many in-situ missions must also cope with long stretches of periodic signal outage during planetary nighttime during which the in-situ probe is unable to communicate with Earth-based ground control at all. Thus, relying solely upon user input to control the system is not only impractical, but potentially detrimental in cases where a fault in the system requires immediate action. Autonomous control offers a solution to this issue by means of partially relegating control to an algorithm or set of algorithms responsible for monitoring mission health, taking preliminary action in the case of a fault, optimizing data transmission, and potentially making mission decisions based on scientific data analysis. Though autonomous control systems for navigation, propulsion, and robotic motion are moderately well-documented in the deep-space context, autonomous control for science tasks, particularly sample analysis on astrobiology missions, poses a more daunting challenge. This is due to the large level of critical thinking – traditionally relegated to humans - involved in analyzing data and making preliminary assessments of what further science tasks need to be done. The author presents an analysis of the levels at which autonomy can be implemented across several subsystems in order to mitigate risk, as well as a risk analysis of the utility of autonomous functionality for astrobiology mission-relevant use-cases.

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Section: Instrument Calibrationmentioning

confidence: 99%

Defining the solution space for autonomous control of in-situ astrobiology missions (IAC-22-A7,3,13,x69116)

Singam¹

2022

Preprint

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“…Many diagnosis approaches have been tried out in industrial use cases. For example in rotor systems (Babu Rao & Mallikarjuna Reddy, 2021;Leitão et al, 2020), smartgrids (Jiang, Zhang, Gao, & Wu, 2014), wind turbines (Svärd & Nyberg, 2011), automotive (Struss, 2002;Stein, 2009), power electronics (Poon et al, 2017), embedded systems (Zoeteweij, Pietersma, Abreu, Feldman, & Van Gemund, 2008), process industry (Kallesoe, Cocquempot, & Izadi-Zamanabadi, 2006;Struss & Ertl, 2009), and spacecraft (Bajwa, Sweet, & Korsmeyer, 2003;Balaban, Narasimhan, Cannon, & Brownston, 2007;Biswas et al, 2016bBiswas et al, , 2016a.…”

Section: State Of the Artmentioning

confidence: 99%

Diagnosing Systems through Approximated Information

Diedrich

Niggemann

2021

PHM_CONF

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This article presents a novel approach to diagnose faults in injection molding machines. A novel data-driven approach is presented to learn an approximation of dependencies between variables using Spearman correlation. It is further shown, how the approximation of the dependencies are used to create propositional logic rules for fault diagnosis. The article presents two novel algorithms: 1) to estimate dependencies from process data and 2) to create propositional logic diagnosis rules from those connections and perform consistency-based fault diagnosis. The presented approach was validated using three experiments. The first two show that the presented approach works well for injection molding machines and a simulation of a four-tank system. The limits of the presented method are shown with the third experiment containing sets of highly correlated signals.

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“…In the aerospace industry, use of artificial intelligence, in particular, model based reasoning, seems to be prevalent [39,40,44]. Although these approaches seem quite advanced, their application to software fault localization may be limited due to the difficulty of producing a reliable model of the software.…”

Section: Other Engineering Fieldsmentioning

confidence: 99%

Interdisciplinary Survey of Fault Localization Techniques to Aid Software Engineering

Beszedes

2019

APH

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Fault localization (narrowing down the cause of a failure to a small number of suspicious components of the system) is an important concern in many different engineering fields and there have been a large number of algorithmic solutions proposed to aid this activity. In this work, we performed a systematic analysis of related literature, not limiting the search to any specific engineering field, with the aim to find solutions in nonsoftware areas that could be successfully adapted to software fault localization. We found out that few areas have significant literature, in this topic, that are good candidates for adaptation (computer networks, for instance), and that although some classes of methods are less suitable, there are useful ideas in almost all fields that could potentially be reused for software fault localization.

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Model-Based Fault Detection and Diagnosis System for NASA Mars Subsurface Drill Prototype

Cited by 7 publications

References 5 publications

Defining the solution space for autonomous control of in-situ astrobiology missions (IAC-22-A7,3,13,x69116)

Defining the solution space for autonomous control of in-situ astrobiology missions (IAC-22-A7,3,13,x69116)

Diagnosing Systems through Approximated Information

Interdisciplinary Survey of Fault Localization Techniques to Aid Software Engineering

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