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The FDI architecture consists of a fault detection estimator and a bank of fault isolation estimators.
The proposed fault diagnosis architecture consists of a fault detection estimator and a bank of isolation estimators, each corresponding to a particular output sensor.
The proposed fault diagnosis architecture consists of a fault detection estimator and a bank of isolation estimators, each corresponding to a particular fault type.
The FDI method follows a general architecture developed in previous papers, where a fault detection estimator is used for fault detection, and a bank of fault isolation estimators are employed to determine the particular fault type/location.
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However, we consider these delays, design distributed memoryless detection estimators and time-varying thresholds for detecting unknown time-delayed interaction faults, and analyze the distributed fault detectability.
Figure 1a is a conventional independent detection and estimation system that consists of an estimator and a detector.
A fault detection and approximation estimator is used for on-line health monitoring.
A detection and approximation estimator is used for online health monitoring.
A multiple differential detection (MDD) sequence estimator, which uses a decision feedback for the demodulation of Gaussian minimum shift keying (GMSK) signals, is described in [7].
In [17], it was shown that edge detection and estimation is analyzed based on forming multiscale point-wise products of smoothed gradient estimators.
In this case, a soft or hard feedback from the detection block to the estimator block performs an iterative channel and data estimation.
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