Real time failure detection algorithm for the Space Shuttle main engine

doi:10.1109/37.608337

Real-time fault detection algorithm for the Space Shuttle Main Engine

10.2514/6.1990-2295 ◽

1990 ◽

Cited By ~ 1

Author(s):

H. PANOSSIAN ◽

V. KEMP ◽

R. NELSON ◽

M. TANIGUCHI

Keyword(s):

Fault Detection ◽

Real Time ◽

Space Shuttle ◽

Detection Algorithm ◽

Main Engine

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Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas

Journal of Diabetes Science and Technology ◽

10.1177/1932296818755173 ◽

2018 ◽

Vol 12 (3) ◽

pp. 599-607 ◽

Cited By ~ 11

Author(s):

Daniel P. Howsmon ◽

Nihat Baysal ◽

Bruce A. Buckingham ◽

Gregory P. Forlenza ◽

Trang T. Ly ◽

...

Keyword(s):

Real Time ◽

Artificial Pancreas ◽

Intervention Strategy ◽

Failure Detection ◽

Detection Algorithm ◽

Outpatient Setting ◽

False Positives ◽

Zone Model ◽

Infusion Site ◽

Detection Algorithms

Background: As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient’s glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures. Methods: An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed. Results: In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58). Conclusions: As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875

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A Simplified Dynamic Model of the Space Shuttle Main Engine

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.2899286 ◽

1994 ◽

Vol 116 (4) ◽

pp. 815-819

Author(s):

Ahmet Duyar ◽

Vasfi Eldem ◽

Walter Merrill ◽

Ten-Huei Guo

Keyword(s):

Input Signal ◽

Space Shuttle ◽

Control Design ◽

Failure Detection ◽

Simplified Model ◽

Nonlinear Simulation ◽

Main Engine ◽

Linked Model ◽

Operating Points ◽

Good Agreement

This paper presents a simplified model of the space shuttle main engine (SSME) dynamics valid within the range of operation of the engine. This model is obtained by linking the linearized point models obtained at 25 different operating points of the SSME. The simplified model was developed for use with a model-based diagnostic scheme for failure detection and diagnostics studies, as well as control design purposes. This simplified model was tested by designing an input signal which covers the whole range of operating points considered and then comparing the responses of linked model and the nonlinear simulation. The comparison indicated good agreement between these responses.

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