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ANNC exploits "representation-based distance" to select the nearest training samples of new fault sample and identifies fault type in a weighting strategy.
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Investigators said they "identified fault conditions that may result in false detection of a panic stop by the Brake Assist system, resulting in unexpected, severe brake application".
Investigators said at the time that they "identified fault conditions that may result in false detection of a panic stop by the brake assist system, resulting in unexpected, severe brake application".
The results demonstrate that the proposed method can identify fault information effectively and accurately.
Important contribution on identifying fault area in a power system is given by C-H Kim. C-H Kim
This paper involves the electric network dynamics to promptly identify fault modes and protection misoperations.
According to these characteristics, an integration criterion identifying fault direction is constructed.
A vote procedure was proposed to identify fault types and conflicts.
Final results give the fault classes set and suitable remedies or a reliability rate of the possible identified fault class.
This method has a limitation that the error in identified fault parameters increases with decrease in number of measured vibrations.
In this paper, we identified fault prediction algorithms based on different machine learning classifiers and distinct feature selection techniques.
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