System Health Management Design Strategies

Day, John C.; Johnson, Stephen B.

doi:10.2514/6.2014-1819

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…If it is desired to protect against that non-covered risk, then some other mechanism must be provided, either FM to predict or detect and respond, or to prevent the failure from occurring by adding design margin (which is the other major aspect of SHM), which would directly lower the probability of occurrence. 7 This yields the following value estimates of the FMCLs in terms of loss of science / loss of mission. The positive way of stating the value is "Loss of Science Benefit", or "Loss of Mission Benefit".…”

Section: G Value Of Fmclsmentioning

confidence: 99%

Fault Management Metrics

Johnson¹,

Ghoshal

Haste

et al. 2017

AIAA Information Systems-Aiaa Infotech @ Aerospace

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This paper describes the theory and considerations in the application of metrics to measure the effectiveness of fault management. Fault management refers here to the operational aspect of system health management, and as such is considered as a meta-control loop that operates to preserve or maximize the system's ability to achieve its goals in the face of current or prospective failure. As a suite of control loops, the metrics to estimate and measure the effectiveness of fault management are similar to those of classical control loops in being divided into two major classes: state estimation, and state control. State estimation metrics can be classified into lower-level subdivisions for detection coverage, detection effectiveness, fault isolation and fault identification (diagnostics), and failure prognosis. State control metrics can be classified into response determination effectiveness and response effectiveness. These metrics are applied to each and every fault management control loop in the system, for each failure to which they apply, and probabilistically summed to determine the effectiveness of these fault management control loops to preserve the relevant system goals that they are intended to protect.

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Section: G Value Of Fmclsmentioning

confidence: 99%

Fault Management Metrics

Johnson¹,

Ghoshal

Haste

et al. 2017

AIAA Information Systems-Aiaa Infotech @ Aerospace

Self Cite

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Abort Trigger False Positive and False Negative Analysis Methodology for Threshold-based Abort Detection

Melcher¹,

Cruz²,

Stephen³

et al. 2015

PHM_CONF

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This paper describes a quantitative methodology for bounding the false positive (FP) and false negative (FN) probabilities associated with a human-rated launch vehicle abort trigger (AT) that includes sensor data qualification (SDQ). In this context, an AT is a hardware and software mechanism designed to detect the existence of a specific abort condition. Also, SDQ is an algorithmic approach used to identify sensor data suspected of being corrupt so that suspect data does not adversely affect an AT’s detection capability. The FP and FN methodologies presented here were developed to support estimation of the probabilities of loss of crew and loss of mission for the Space Launch System (SLS) which is being developed by the National Aeronautics and Space Administration (NASA). The paper provides a brief overview of system health management as being an extension of control theory; and describes how ATs and the calculation of FP and FN probabilities relate to this theory. The discussion leads to a detailed presentation of the FP and FN methodology and an example showing how the FP and FN calculations are performed. This detailed presentation includes a methodology for calculating the change in FP and FN probabilities that result from including SDQ in the AT architecture. To avoid proprietary and sensitive data issues, the example incorporates a mixture of open literature and fictitious reliability data. Results presented in the paper demonstrate the effectiveness of the approach in providing quantitative estimates that bound the probability of a FP or FN abort determination.

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System Health Management Design Strategies

Cited by 2 publications

References 3 publications

Fault Management Metrics

Fault Management Metrics

Abort Trigger False Positive and False Negative Analysis Methodology for Threshold-based Abort Detection

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