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In the original fault isolation methods, contribution plots are popular.
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This paper analyzed the feasibility of the converter-based fault isolation method, including the isolating speed, influence on the ac-system protection and restart of the system.
Therefore, the converter-based fault isolation method is widely discussed.
This work designs a robust fault isolation method applicable to the directional residual approach by using decision theory.
To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model.
Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables.
According to nonlinear representation via a gain scheduling interpolation method (successfully used in the control design even with fast varying parameters), the design is performed by extending robust fault isolation filter methods in LTI systems to nonlinear systems.
Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain.
The model is particularly suited to compare and contrast a wide range of fault detection and isolation methods within a strongly oriented focus on application to industrial actuator examples.
The fault detection and isolation method has been applied by injecting simulated faults to flight data collected by a semi-scale YF-22 research aircraft model.
In this paper, the fault isolation and fault magnitude estimate methods are proposed.
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