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In the current study, fault detection settings successfully identified sensor failure.
Further research is needed to optimize automated fault detection settings and to ensure current mechanisms are effective.
Further study is necessary to determine the optimum fault detection settings to maximize patient safety yet minimize disruption to the operation of the closed-loop system.
The PGCS used fault detection settings regarding 1) correlation of sensor signals, 2) deviation of sensor glucose (SG1 and SG2) from the mean (SG), 3) mutual difference in rate of glucose change (direction of trend), 4) loss of sensor signal, RF, or Bluetooth connectivity, and 5) insulin delivery limits.
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Design procedure incorporates sensor cost, fault detectability and fault detection rate in the design formulation.
The fault detection algorithm can detect the presence of a fault within one power cycle.
Once the fault is detected by the Fault Detection and Isolation (FDI) unit, a fault compensation loop is activated.
The faults degrading the system performance are detected by the performance-based fault detection mechanism.
Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms.
As a case study, a user interface (UI) system, including an overview panel and alarm settings for plant operations, was evaluated from the viewpoints of shortening fault detection and identification (FDI) track and decreasing workloads.
Two distinct sets of fault indicators are determined, one for fault detection, he other one for fault isolation.
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