Optimal Sensor Selection for Health Monitoring Systems

Santi, L. Michael; Sowers, T. Shane; Aguilar, Robert

doi:10.2514/6.2005-4485

Cited by 43 publications

(21 citation statements)

References 13 publications

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“…Santi (ref. 4) and Butas (ref. 10) proposed an approach where specific system health parameters from available simulations are extracted to establish a fault data reconciliation matrix.…”

Section: Fault Detectionmentioning

confidence: 99%

Propulsion Health Management System Development for Affordable and Reliable Operation of Space Exploration Systems

Melcher

Maul²,

Garg

2007

AIAA SPACE 2007 Conference &Amp; Exposition

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“…Santi (ref. 4) and Butas (ref. 10) proposed an approach where specific system health parameters from available simulations are extracted to establish a fault data reconciliation matrix.…”

Section: Fault Detectionmentioning

confidence: 99%

Propulsion Health Management System Development for Affordable and Reliable Operation of Space Exploration Systems

Melcher

Maul²,

Garg

2007

AIAA SPACE 2007 Conference &Amp; Exposition

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“…As noted by both Santi (ref. 13) and Musulin (ref. 10), the fidelity of the model utilized will impact the performance, health assessment, or data reconciliation, and therefore the sensor suite selection process.…”

Section: A Objectivesmentioning

confidence: 99%

“…13) utilized a high-fidelity dynamic model of a rocket propulsion system to generate a simplified linear model. Health parameters were identified within the linear model that represented specific fault conditions.…”

Section: Fault Detectability/fault Discriminabilitymentioning

confidence: 99%

Sensor Selection and Optimization for Health Assessment of Aerospace Systems

Maul

Kopasakis

Santi

et al. 2008

Journal of Aerospace Computing, Information, and Communication

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“…References [13][14][15][16][17] provide context for this section. For example, the Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively identifying the sensor compliment that optimally achieves the health assessment goals of a system.…”

Section: Intelligent Sensors and Componentsmentioning

confidence: 99%

Integrated Systems Health Management for Intelligent Systems

Figueroa¹,

Melcher²

2012

Advances in Intelligent and Autonomous Aerospace Systems

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The implementation of an integrated system health management (ISHM) capability is fundamentally linked to the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system. It is akin to having a team of experts who are all individually and collectively observing and analyzing a complex system, and communicating effectively with each other in order to arrive at an accurate and reliable assessment of its health. In this paper, concepts, procedures, and approaches are presented as a foundation for implementing an intelligent systems-relevant ISHM capability. The capability stresses integration of DIaK from all elements of a system. Both ground-based (remote) and on-board ISHM capabilities are compared and contrasted. The information presented is the result of many years of research, development, and maturation of technologies, and of prototype implementations in operational systems. Purpose/MotivationIn this paper, Integrated Systems Health Management (ISHM) is presented as an enabling technology for intelligent systems. To that end, a variety of intelligent systems-relevant ISHM topics are addressed and examples presented. The information presented should provide the reader with an understanding of current research and challenges that are relevant to ISHM as one element of an intelligent system. ISHM has been defined from many perspectives. Here it is defined as a capability that is achieved by integrating data, information, and knowledge (DIaK) that might be distributed throughout the system elements (which inherently implies the capability to manage DIaK associated with distributed subsystems). DIaK must be available to any element of a system at the right time and in accordance with a meaningful context. ISHM Functional Capability Level (FCL) is measured by how well a system performs the following functions: (1) detect anomalies, (2) diagnose causes, (3) predict future anomalies/failures, and (4) provide the user with an integrated awareness about the condition of important elements of the system as a means of guiding user decisions. Scope/OrganizationThe paper opens with a glossary of relevant ISHM terms. Because the development of an ISHM system is interdisciplinary by nature, terminology is seldom consistently used. It is, therefore important to define the basic terminology that will be used to support ensuing discussions. The paper then describes a process for developing an ISHM capability -standards, qualitative failure models, and optimization of sensor selection and placement are addressed as a part of the development process. Next, an ISHM knowledge domain model is defined that is particularly suited to intelligent systems. This model is based on Data, Information, and Knowledge (DIaK) of the system. This knowledge model is used as the basis for Intelligent ISHM architectures for ground-based (remote) and onboard flight (manned and unmanned) systems. Elements (e.g., diagnostics, prognostics) of the architectures are subsequently descri...

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Optimal Sensor Selection for Health Monitoring Systems

Cited by 43 publications

References 13 publications

Propulsion Health Management System Development for Affordable and Reliable Operation of Space Exploration Systems

Propulsion Health Management System Development for Affordable and Reliable Operation of Space Exploration Systems

Sensor Selection and Optimization for Health Assessment of Aerospace Systems

Integrated Systems Health Management for Intelligent Systems

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